This fix is for #21726. When having other packages installed that
require the `openai_api_base` environment variable, users are not able
to instantiate the AzureChatModels or AzureEmbeddings.
This PR adds a new value `ignore_openai_api_base` which is a bool. When
set to True, it sets `openai_api_base` to `None`
Two new tests were added for the `test_azure` and a new file
`test_azure_embeddings`
A different approach may be better for this. If you can think of better
logic, let me know and I can adjust it.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Fix#23716
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR introduces a maxsize parameter for the InMemoryCache class,
allowing users to specify the maximum number of items to store in the
cache. If the cache exceeds the specified maximum size, the oldest items
are removed. Additionally, comprehensive unit tests have been added to
ensure all functionalities are thoroughly tested. The tests are written
using pytest and cover both synchronous and asynchronous methods.
Twitter: @spyrosavl
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Fix LLM string representation for serializable objects.
Fix for issue: https://github.com/langchain-ai/langchain/issues/23257
The llm string of serializable chat models is the serialized
representation of the object. LangChain serialization dumps some basic
information about non serializable objects including their repr() which
includes an object id.
This means that if a chat model has any non serializable fields (e.g., a
cache), then any new instantiation of the those fields will change the
llm representation of the chat model and cause chat misses.
i.e., re-instantiating a postgres cache would result in cache misses!
**Description:** In the chat_models module of the language model, the
import statement for BaseModel has been moved from the conditionally
imported section to the main import area, fixing `NameError `.
**Issue:** fix `NameError `
- Description: Modified the prompt created by the function
`create_unstructured_prompt` (which is called for LLMs that do not
support function calling) by adding conditional checks that verify if
restrictions on entity types and rel_types should be added to the
prompt. If the user provides a sufficiently large text, the current
prompt **may** fail to produce results in some LLMs. I have first seen
this issue when I implemented a custom LLM class that did not support
Function Calling and used Gemini 1.5 Pro, but I was able to replicate
this issue using OpenAI models.
By loading a sufficiently large text
```python
from langchain_community.llms import Ollama
from langchain_openai import ChatOpenAI, OpenAI
from langchain_core.prompts import PromptTemplate
import re
from langchain_experimental.graph_transformers import LLMGraphTransformer
from langchain_core.documents import Document
with open("texto-longo.txt", "r") as file:
full_text = file.read()
partial_text = full_text[:4000]
documents = [Document(page_content=partial_text)] # cropped to fit GPT 3.5 context window
```
And using the chat class (that has function calling)
```python
chat_openai = ChatOpenAI(model="gpt-3.5-turbo", model_kwargs={"seed": 42})
chat_gpt35_transformer = LLMGraphTransformer(llm=chat_openai)
graph_from_chat_gpt35 = chat_gpt35_transformer.convert_to_graph_documents(documents)
```
It works:
```
>>> print(graph_from_chat_gpt35[0].nodes)
[Node(id="Jesu, Joy of Man's Desiring", type='Music'), Node(id='Godel', type='Person'), Node(id='Johann Sebastian Bach', type='Person'), Node(id='clever way of encoding the complicated expressions as numbers', type='Concept')]
```
But if you try to use the non-chat LLM class (that does not support
function calling)
```python
openai = OpenAI(
model="gpt-3.5-turbo-instruct",
max_tokens=1000,
)
gpt35_transformer = LLMGraphTransformer(llm=openai)
graph_from_gpt35 = gpt35_transformer.convert_to_graph_documents(documents)
```
It uses the prompt that has issues and sometimes does not produce any
result
```
>>> print(graph_from_gpt35[0].nodes)
[]
```
After implementing the changes, I was able to use both classes more
consistently:
```shell
>>> chat_gpt35_transformer = LLMGraphTransformer(llm=chat_openai)
>>> graph_from_chat_gpt35 = chat_gpt35_transformer.convert_to_graph_documents(documents)
>>> print(graph_from_chat_gpt35[0].nodes)
[Node(id="Jesu, Joy Of Man'S Desiring", type='Music'), Node(id='Johann Sebastian Bach', type='Person'), Node(id='Godel', type='Person')]
>>> gpt35_transformer = LLMGraphTransformer(llm=openai)
>>> graph_from_gpt35 = gpt35_transformer.convert_to_graph_documents(documents)
>>> print(graph_from_gpt35[0].nodes)
[Node(id='I', type='Pronoun'), Node(id="JESU, JOY OF MAN'S DESIRING", type='Song'), Node(id='larger memory', type='Memory'), Node(id='this nice tree structure', type='Structure'), Node(id='how you can do it all with the numbers', type='Process'), Node(id='JOHANN SEBASTIAN BACH', type='Composer'), Node(id='type of structure', type='Characteristic'), Node(id='that', type='Pronoun'), Node(id='we', type='Pronoun'), Node(id='worry', type='Verb')]
```
The results are a little inconsistent because the GPT 3.5 model may
produce incomplete json due to the token limit, but that could be solved
(or mitigated) by checking for a complete json when parsing it.
This PR adds a part of the indexing API proposed in this RFC
https://github.com/langchain-ai/langchain/pull/23544/files.
It allows rolling out `get_by_ids` which should be uncontroversial to
existing vectorstores without introducing new abstractions.
The semantics for this method depend on the ability of identifying
returned documents using the new optional ID field on documents:
https://github.com/langchain-ai/langchain/pull/23411
Alternatives are:
1. Relax the sequence requirement
```python
def get_by_ids(self, ids: Iterable[str], /) -> Iterable[Document]:
```
Rejected:
- implementations are more likley to start batching with bad defaults
- users would need to call list() or we'd need to introduce another
convenience method
2. Support more kwargs
```python
def get_by_ids(self, ids: Sequence[str], /, **kwargs) -> List[Document]:
...
```
Rejected:
- No need for `batch` parameter since IDs is a sequence
- Output cannot be customized since `Document` is fixed. (e.g.,
parameters could be useful to grab extra metadata like the vector that
was indexed with the Document or to project a part of the document)
**Description:** LanceDB didn't allow querying the database using
similarity score thresholds because the metrics value was missing. This
PR simply fixes that bug.
**Issue:** not applicable
**Dependencies:** none
**Twitter handle:** not available
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description:** At the moment the Jira wrapper only accepts the the
usage of the Username and Password/Token at the same time. However Jira
allows the connection using only is useful for enterprise context.
Co-authored-by: rpereira <rafael.pereira@criticalsoftware.com>
After merging the [PR #22594 to include Jina AI multimodal capabilities
in the Langchain
documentation](https://github.com/langchain-ai/langchain/pull/22594), we
updated the notebook to showcase the difference between text and
multimodal capabilities more clearly.
DOC: missing parenthesis #23687
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- Update Meta Llama 3 cookbook link
- Add prereq section and information on `messages_modifier` to LangGraph
migration guide
- Update `PydanticToolsParser` explanation and entrypoint in tool
calling guide
- Add more obvious warning to `OllamaFunctions`
- Fix Wikidata tool install flow
- Update Bedrock LLM initialization
@baskaryan can you add a bit of information on how to authenticate into
the `ChatBedrock` and `BedrockLLM` models? I wasn't able to figure it
out :(
This change adds a new message type `RemoveMessage`. This will enable
`langgraph` users to manually modify graph state (or have the graph
nodes modify the state) to remove messages by `id`
Examples:
* allow users to delete messages from state by calling
```python
graph.update_state(config, values=[RemoveMessage(id=state.values[-1].id)])
```
* allow nodes to delete messages
```python
graph.add_node("delete_messages", lambda state: [RemoveMessage(id=state[-1].id)])
```
- add test for structured output
- fix bug with structured output for Azure
- better testing on Groq (break out Mixtral + Llama3 and add xfails
where needed)
This PR modifies the API Reference in the following way:
1. Relist standard methods: invoke, ainvoke, batch, abatch,
batch_as_completed, abatch_as_completed, stream, astream,
astream_events. These are the main entry points for a lot of runnables,
so we'll keep them for each runnable.
2. Relist methods from Runnable Serializable: to_json,
configurable_fields, configurable_alternatives.
3. Expand the note in the API reference documentation to explain that
additional methods are available.
- **Description:** The name of ToolMessage is default to None, which
makes tool message send to LLM likes
```json
{"role": "tool",
"tool_call_id": "",
"content": "{\"time\": \"12:12\"}",
"name": null}
```
But the name seems essential for some LLMs like TongYi Qwen. so we need to set the name use agent_action's tool value.
- **Issue:** N/A
- **Dependencies:** N/A
- **Description:** Fixing the way users have to import Arxiv and
Semantic Scholar
- **Issue:** Changed to use `from langchain_community.tools.arxiv import
ArxivQueryRun` instead of `from langchain_community.tools.arxiv.tool
import ArxivQueryRun`
- **Dependencies:** None
- **Twitter handle:** Nope
This PR fixes an issue with not able to use unlimited/infinity tokens
from the respective provider for the LiteLLM provider.
This is an issue when working in an agent environment that the token
usage can drastically increase beyond the initial value set causing
unexpected behavior.
Descriptions: currently in the
[doc](https://python.langchain.com/v0.2/docs/how_to/extraction_examples/)
it sets "Data" as the LLM's structured output schema, however its
examples given to the LLM output's "Person", which causes the LLM to be
confused and might occasionally return "Person" as the function to call
issue: #23383
Co-authored-by: Lifu Wu <lifu@nextbillion.ai>
- **Description:** A small fix where I moved the `available_endpoints`
in order to avoid the token error in the below issue. Also I have added
conftest file and updated the `scripy`,`numpy` versions to support newer
python versions in poetry files.
- **Issue:** #22804
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: ccurme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Discovered alongside @t968914
- **Description:**
According to OpenAI docs, tool messages (response from calling tools)
must have a 'name' field.
https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models
- **Issue:** N/A (as of right now)
- **Dependencies:** N/A
- **Twitter handle:** N/A
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
This PR adds an optional ID field to the document schema.
# 1. Optional or Required
- An optional field will will requrie additional checking for the type
in user code (annoying).
- However, vectorstores currently don't respect this field. So if we
make it
required and start returning random UUIDs that might be even more
confusing
to users.
**Proposal**: Start with Optional and convert to Required (with default
set to uuid4()) in 1-2 major releases.
# 2. Override __str__ or generic solution in prompts
Overriding __str__ as a simple way to avoid changing user code that
relies on
default str(document) in prompts.
I considered rolling out a more general solution in prompts
(https://github.com/langchain-ai/langchain/pull/8685),
but to do that we need to:
1. Make things serializable
2. The more general solution would likely need to be backwards
compatible as well
3. It's unclear that one wants to format a List[int] in the same way as
List[Document]. The former should be `,` seperated (likely), the latter
should be `---` separated (likely).
**Proposal** Start with __str__ override and focus on the vectorstore
APIs, we generalize prompts later
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- Updates chat few shot prompt tutorial to show off a more cohesive
example
- Fix async Chromium loader guide
- Fix Excel loader install instructions
- Reformat Html2Text page
- Add install instructions to Azure OpenAI embeddings page
- Add missing dep install to SQL QA tutorial
@baskaryan
## Description
Created a helper method to make vector search indexes via client-side
pymongo.
**Recent Update** -- Removed error suppressing/overwriting layer in
favor of letting the original exception provide information.
## ToDo's
- [x] Make _wait_untils for integration test delete index
functionalities.
- [x] Add documentation for its use. Highlight it's experimental
- [x] Post Integration Test Results in a screenshot
- [x] Get review from MongoDB internal team (@shaneharvey, @blink1073 ,
@NoahStapp , @caseyclements)
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. Added new integration tests. Not eligible for unit testing since the
operation is Atlas Cloud specific.
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Thank you for contributing to LangChain!
- [X] **PR title**: "community: fix code example in ZenGuard docs"
- [X] **PR message**:
- **Description:** corrected the docs by indicating in the code example
that the tool accepts a list of prompts instead of just one
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Thank you for review
---------
Co-authored-by: Baur <baur.krykpayev@gmail.com>
- **Description:** This PR fixes an issue with SAP HANA Cloud QRC03
version. In that version the number to indicate no length being set for
a vector column changed from -1 to 0. The change in this PR support both
behaviours (old/new).
- **Dependencies:** No dependencies have been introduced.
- **Tests**: The change is covered by previous unit tests.
fixed potential `IndexError: list index out of range` in case there is
no title
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**langchain: ConversationVectorStoreTokenBufferMemory**
-**Description:** This PR adds ConversationVectorStoreTokenBufferMemory.
It is similar in concept to ConversationSummaryBufferMemory. It
maintains an in-memory buffer of messages up to a preset token limit.
After the limit is hit timestamped messages are written into a
vectorstore retriever rather than into a summary. The user's prompt is
then used to retrieve relevant fragments of the previous conversation.
By persisting the vectorstore, one can maintain memory from session to
session.
-**Issue:** n/a
-**Dependencies:** none
-**Twitter handle:** Please no!!!
- [X] **Add tests and docs**: I looked to see how the unit tests were
written for the other ConversationMemory modules, but couldn't find
anything other than a test for successful import. I need to know whether
you are using pytest.mock or another fixture to simulate the LLM and
vectorstore. In addition, I would like guidance on where to place the
documentation. Should it be a notebook file in docs/docs?
- [X] **Lint and test**: I am seeing some linting errors from a couple
of modules unrelated to this PR.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: update docs and add tool to init.py"
- [x] **PR message**:
- **Description:** Fixed some errors and comments in the docs and added
our ZenGuardTool and additional classes to init.py for easy access when
importing
- **Question:** when will you update the langchain-community package in
pypi to make our tool available?
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Thank you for review!
---------
Co-authored-by: Baur <baur.krykpayev@gmail.com>
These currently read off AIMessage.tool_calls, and only fall back to
OpenAI parsing if tool calls aren't populated.
Importing these from `openai_tools` (e.g., in our [tool calling
docs](https://python.langchain.com/v0.2/docs/how_to/tool_calling/#tool-calls))
can lead to confusion.
After landing, would need to release core and update docs.
Pydantic allows empty strings:
```
from langchain.pydantic_v1 import Field, BaseModel
class Property(BaseModel):
"""A single property consisting of key and value"""
key: str = Field(..., description="key")
value: str = Field(..., description="value")
x = Property(key="", value="")
```
Which can produce errors downstream. We simply ignore those records
bing_search_url is an endpoint to requests bing search resource and is
normally invariant to users, we can give it the default value to simply
the uesages of this utility/tool
Description: Add classifier_location feature flag. This flag enables
Pebblo to decide the classifier location, local or pebblo-cloud.
Unit Tests: N/A
Documentation: N/A
---------
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
The code snippet under ‘pdfs_qa’ contains an small incorrect code
example , resulting in users getting errors. This pr replaces ‘llm’
variable with ‘model’ to help user avoid a NameError message.
Resolves#22689
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Adds options for configuring MongoDBChatMessageHistory
(no breaking changes):
- session_id_key: name of the field that stores the session id
- history_key: name of the field that stores the chat history
- create_index: whether to create an index on the session id field
- index_kwargs: additional keyword arguments to pass to the index
creation
**Discussion:**
https://github.com/langchain-ai/langchain/discussions/22918
**Twitter handle:** @userlerueda
---------
Co-authored-by: Jib <Jibzade@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Add standard tests to base store abstraction. These only work on [str,
str] right now. We'll need to check if it's possible to add
encoder/decoders to generalize
**Description:**
This PR addresses an issue in the `MongodbLoader` where nested fields
were not being correctly extracted. The loader now correctly handles
nested fields specified in the `field_names` parameter.
**Issue:**
Fixes an issue where attempting to extract nested fields from MongoDB
documents resulted in `KeyError`.
**Dependencies:**
No new dependencies are required for this change.
**Twitter handle:**
(Optional, your Twitter handle if you'd like a mention when the PR is
announced)
### Changes
1. **Field Name Parsing**:
- Added logic to parse nested field names and safely extract their
values from the MongoDB documents.
2. **Projection Construction**:
- Updated the projection dictionary to include nested fields correctly.
3. **Field Extraction**:
- Updated the `aload` method to handle nested field extraction using a
recursive approach to traverse the nested dictionaries.
### Example Usage
Updated usage example to demonstrate how to specify nested fields in the
`field_names` parameter:
```python
loader = MongodbLoader(
connection_string=MONGO_URI,
db_name=MONGO_DB,
collection_name=MONGO_COLLECTION,
filter_criteria={"data.job.company.industry_name": "IT", "data.job.detail": { "$exists": True }},
field_names=[
"data.job.detail.id",
"data.job.detail.position",
"data.job.detail.intro",
"data.job.detail.main_tasks",
"data.job.detail.requirements",
"data.job.detail.preferred_points",
"data.job.detail.benefits",
],
)
docs = loader.load()
print(len(docs))
for doc in docs:
print(doc.page_content)
```
### Testing
Tested with a MongoDB collection containing nested documents to ensure
that the nested fields are correctly extracted and concatenated into a
single page_content string.
### Note
This change ensures backward compatibility for non-nested fields and
improves functionality for nested field extraction.
### Output Sample
```python
print(docs[:3])
```
```shell
# output sample:
[
Document(
# Here in this example, page_content is the combined text from the fields below
# "position", "intro", "main_tasks", "requirements", "preferred_points", "benefits"
page_content='all combined contents from the requested fields in the document',
metadata={'database': 'Your Database name', 'collection': 'Your Collection name'}
),
...
]
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] PR title:
community: Add OCI Generative AI new model support
- [x] PR message:
- Description: adding support for new models offered by OCI Generative
AI services. This is a moderate update of our initial integration PR
16548 and includes a new integration for our chat models under
/langchain_community/chat_models/oci_generative_ai.py
- Issue: NA
- Dependencies: No new Dependencies, just latest version of our OCI sdk
- Twitter handle: NA
- [x] Add tests and docs:
1. we have updated our unit tests
2. we have updated our documentation including a new ipynb for our new
chat integration
- [x] Lint and test:
`make format`, `make lint`, and `make test` run successfully
---------
Co-authored-by: RHARPAZ <RHARPAZ@RHARPAZ-5750.us.oracle.com>
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
** Description**
This is the community integration of ZenGuard AI - the fastest
guardrails for GenAI applications. ZenGuard AI protects against:
- Prompts Attacks
- Veering of the pre-defined topics
- PII, sensitive info, and keywords leakage.
- Toxicity
- Etc.
**Twitter Handle** : @zenguardai
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. Added an integration test
2. Added colab
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
---------
Co-authored-by: Nuradil <nuradil.maksut@icloud.com>
Co-authored-by: Nuradil <133880216+yaksh0nti@users.noreply.github.com>
They are now rejecting with code 401 calls from users with expired or
invalid tokens (while before they were being considered anonymous).
Thus, the authorization header has to be removed when there is no token.
Related to: #23178
---------
Signed-off-by: Joffref <mariusjoffre@gmail.com>
Description: 2 feature flags added to SharePointLoader in this PR:
1. load_auth: if set to True, adds authorised identities to metadata
2. load_extended_metadata, adds source, owner and full_path to metadata
Unit tests:N/A
Documentation: To be done.
---------
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
This fixes processing issue for nodes with numbers in their labels (e.g.
`"node_1"`, which would previously be relabeled as `"node__"`, and now
are correctly processed as `"node_1"`)
**Description:**
Fix "`TypeError: 'NoneType' object is not iterable`" when the
auth_context is absent in PebbloRetrievalQA. The auth_context is
optional; hence, PebbloRetrievalQA should work without it, but it throws
an error at the moment. This PR fixes that issue.
**Issue:** NA
**Dependencies:** None
**Unit tests:** NA
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: file_metadata_ was not getting propagated to returned
documents. Changed the lookup key to the name of the blob's path.
Changed blob.path key to blob.path.name for metadata_dict key lookup.
Documentation: N/A
Unit tests: N/A
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:**
Currently, the `langchain_pinecone` library forces the `async_req`
(asynchronous required) argument to Pinecone to `True`. This design
choice causes problems when deploying to environments that do not
support multiprocessing, such as AWS Lambda. In such environments, this
restriction can prevent users from successfully using
`langchain_pinecone`.
This PR introduces a change that allows users to specify whether they
want to use asynchronous requests by passing the `async_req` parameter
through `**kwargs`. By doing so, users can set `async_req=False` to
utilize synchronous processing, making the library compatible with AWS
Lambda and other environments that do not support multithreading.
**Issue:**
This PR does not address a specific issue number but aims to resolve
compatibility issues with AWS Lambda by allowing synchronous processing.
**Dependencies:**
None, that I'm aware of.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** When use
RunnableWithMessageHistory/SQLChatMessageHistory in async mode, we'll
get the following error:
```
Error in RootListenersTracer.on_chain_end callback: RuntimeError("There is no current event loop in thread 'asyncio_3'.")
```
which throwed by
ddfbca38df/libs/community/langchain_community/chat_message_histories/sql.py (L259).
and no message history will be add to database.
In this patch, a new _aexit_history function which will'be called in
async mode is added, and in turn aadd_messages will be called.
In this patch, we use `afunc` attribute of a Runnable to check if the
end listener should be run in async mode or not.
- **Issue:** #22021, #22022
- **Dependencies:** N/A
The SelfQuery PGVectorTranslator is not correct. The operator is "eq"
and not "$eq".
This patch use a new version of PGVectorTranslator from
langchain_postgres.
It's necessary to release a new version of langchain_postgres (see
[here](https://github.com/langchain-ai/langchain-postgres/pull/75)
before accepting this PR in langchain.
fix systax warning in `create_json_chat_agent`
```
.../langchain/agents/json_chat/base.py:22: SyntaxWarning: invalid escape sequence '\ '
"""Create an agent that uses JSON to format its logic, build for Chat Models.
```
- **Description:** AsyncRootListenersTracer support on_chat_model_start,
it's schema_format should be "original+chat".
- **Issue:** N/A
- **Dependencies:**
minor changes to module import error handling and minor issues in
tutorial documents.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
**Desscription**: When the ``sql_database.from_databricks`` is executed
from a Workflow Job, the ``context`` object does not have a
"browserHostName" property, resulting in an error. This change manages
the error so the "DATABRICKS_HOST" env variable value is used instead of
stoping the flow
Co-authored-by: lmorosdb <lmorosdb>
The return type of `json.loads` is `Any`.
In fact, the return type of `dumpd` must be based on `json.loads`, so
the correction here is understandable.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Fix bug with TypedDicts rendering inherited methods if inherting from
typing_extensions.TypedDict rather than typing.TypedDict
- Do not surface inherited pydantic methods for subclasses of BaseModel
- Subclasses of RunnableSerializable will not how methods inherited from
Runnable or from BaseModel
- Subclasses of Runnable that not pydantic models will include a link to
RunnableInterface (they still show inherited methods, we can fix this
later)
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Description: Update Rag tutorial notebook so it includes an additional
notebook cell with pip installs of required langchain_chroma and
langchain_community.
This fixes the issue with the rag tutorial gives you a 'missing modules'
error if you run code in the notebook as is.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Add optional max_messages to MessagePlaceholder
- **Issue:**
[16096](https://github.com/langchain-ai/langchain/issues/16096)
- **Dependencies:** None
- **Twitter handle:** @davedecaprio
Sometimes it's better to limit the history in the prompt itself rather
than the memory. This is needed if you want different prompts in the
chain to have different history lengths.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Thank you for contributing to LangChain!
**Description**
The current code snippet for `Fireworks` had incorrect parameters. This
PR fixes those parameters.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
currently we skip CI on diffs >= 300 files. think we should just run it
on all packages instead
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- Moved doc-strings below attribtues in TypedDicts -- seems to render
better on APIReference pages.
* Provided more description and some simple code examples
- **Description:** Restores compatibility with SQLAlchemy 1.4.x that was
broken since #18992 and adds a test run for this version on CI (only for
Python 3.11)
- **Issue:** fixes#19681
- **Dependencies:** None
- **Twitter handle:** `@krassowski_m`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** sambanova sambaverse integration improvement: removed
input parsing that was changing raw user input, and was making to use
process prompt parameter as true mandatory
**Description:** `astream_events(version="v2")` didn't propagate
exceptions in `langchain-core<=0.2.6`, fixed in the #22916. This PR adds
a unit test to check that exceptions are propagated upwards.
Co-authored-by: Sergey Kozlov <sergey.kozlov@ludditelabs.io>
Added missed docstrings. Format docstrings to the consistent format
(used in the API Reference)
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
This raises ImportError due to a circular import:
```python
from langchain_core import chat_history
```
This does not:
```python
from langchain_core import runnables
from langchain_core import chat_history
```
Here we update `test_imports` to run each import in a separate
subprocess. Open to other ways of doing this!
Tests failing on master with
> FAILED
tests/unit_tests/embeddings/test_ovhcloud.py::test_ovhcloud_embed_documents
- ValueError: Request failed with status code: 401, {"message":"Bad
token; invalid JSON"}
Thank you for contributing to LangChain!
**Description:** Noticed an issue with when I was calling
`RecursiveJsonSplitter().split_json()` multiple times that I was getting
weird results. I found an issue where `chunks` list in the `_json_split`
method. If chunks is not provided when _json_split (which is the case
when split_json calls _json_split) then the same list is used for
subsequent calls to `_json_split`.
You can see this in the test case i also added to this commit.
Output should be:
```
[{'a': 1, 'b': 2}]
[{'c': 3, 'd': 4}]
```
Instead you get:
```
[{'a': 1, 'b': 2}]
[{'a': 1, 'b': 2, 'c': 3, 'd': 4}]
```
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
- **Description:** add `**request_kwargs` and expect `TimeError` in
`_fetch` function for AsyncHtmlLoader. This allows you to fill in the
kwargs parameter when using the `load()` method of the `AsyncHtmlLoader`
class.
Co-authored-by: Yucolu <yucolu@tencent.com>
#### Description
This MR defines a `ExperimentalMarkdownSyntaxTextSplitter` class. The
main goal is to replicate the functionality of the original
`MarkdownHeaderTextSplitter` which extracts the header stack as metadata
but with one critical difference: it keeps the whitespace of the
original text intact.
This draft reimplements the `MarkdownHeaderTextSplitter` with a very
different algorithmic approach. Instead of marking up each line of the
text individually and aggregating them back together into chunks, this
method builds each chunk sequentially and applies the metadata to each
chunk. This makes the implementation simpler. However, since it's
designed to keep white space intact its not a full drop in replacement
for the original. Since it is a radical implementation change to the
original code and I would like to get feedback to see if this is a
worthwhile replacement, should be it's own class, or is not a good idea
at all.
Note: I implemented the `return_each_line` parameter but I don't think
it's a necessary feature. I'd prefer to remove it.
This implementation also adds the following additional features:
- Splits out code blocks and includes the language in the `"Code"`
metadata key
- Splits text on the horizontal rule `---` as well
- The `headers_to_split_on` parameter is now optional - with sensible
defaults that can be overridden.
#### Issue
Keeping the whitespace keeps the paragraphs structure and the formatting
of the code blocks intact which allows the caller much more flexibility
in how they want to further split the individuals sections of the
resulting documents. This addresses the issues brought up by the
community in the following issues:
- https://github.com/langchain-ai/langchain/issues/20823
- https://github.com/langchain-ai/langchain/issues/19436
- https://github.com/langchain-ai/langchain/issues/22256
#### Dependencies
N/A
#### Twitter handle
@RyanElston
---------
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
# Description
This pull request aims to address specific issues related to the
ambiguity and error-proneness of the output types of certain output
parsers, as well as the absence of unit tests for some parsers. These
issues could potentially lead to runtime errors or unexpected behaviors
due to type mismatches when used, causing confusion for developers and
users. Through clarifying output types, this PR seeks to improve the
stability and reliability.
Therefore, this pull request
- fixes the `OutputType` of OutputParsers to be the expected type;
- e.g. `OutputType` property of `EnumOutputParser` raises `TypeError`.
This PR introduce a logic to extract `OutputType` from its attribute.
- and fixes the legacy API in OutputParsers like `LLMChain.run` to the
modern API like `LLMChain.invoke`;
- Note: For `OutputFixingParser`, `RetryOutputParser` and
`RetryWithErrorOutputParser`, this PR introduces `legacy` attribute with
False as default value in order to keep the backward compatibility
- and adds the tests for the `OutputFixingParser` and
`RetryOutputParser`.
The following table shows my expected output and the actual output of
the `OutputType` of OutputParsers.
I have used this table to fix `OutputType` of OutputParsers.
| Class Name of OutputParser | My Expected `OutputType` (after this PR)|
Actual `OutputType` [evidence](#evidence) (before this PR)| Fix Required
|
|---------|--------------|---------|--------|
| BooleanOutputParser | `<class 'bool'>` | `<class 'bool'>` | NO |
| CombiningOutputParser | `typing.Dict[str, Any]` | `TypeError` is
raised | YES |
| DatetimeOutputParser | `<class 'datetime.datetime'>` | `<class
'datetime.datetime'>` | NO |
| EnumOutputParser(enum=MyEnum) | `MyEnum` | `TypeError` is raised | YES
|
| OutputFixingParser | The same type as `self.parser.OutputType` | `~T`
| YES |
| CommaSeparatedListOutputParser | `typing.List[str]` |
`typing.List[str]` | NO |
| MarkdownListOutputParser | `typing.List[str]` | `typing.List[str]` |
NO |
| NumberedListOutputParser | `typing.List[str]` | `typing.List[str]` |
NO |
| JsonOutputKeyToolsParser | `typing.Any` | `typing.Any` | NO |
| JsonOutputToolsParser | `typing.Any` | `typing.Any` | NO |
| PydanticToolsParser | `typing.Any` | `typing.Any` | NO |
| PandasDataFrameOutputParser | `typing.Dict[str, Any]` | `TypeError` is
raised | YES |
| PydanticOutputParser(pydantic_object=MyModel) | `<class
'__main__.MyModel'>` | `<class '__main__.MyModel'>` | NO |
| RegexParser | `typing.Dict[str, str]` | `TypeError` is raised | YES |
| RegexDictParser | `typing.Dict[str, str]` | `TypeError` is raised |
YES |
| RetryOutputParser | The same type as `self.parser.OutputType` | `~T` |
YES |
| RetryWithErrorOutputParser | The same type as `self.parser.OutputType`
| `~T` | YES |
| StructuredOutputParser | `typing.Dict[str, Any]` | `TypeError` is
raised | YES |
| YamlOutputParser(pydantic_object=MyModel) | `MyModel` | `~T` | YES |
NOTE: In "Fix Required", "YES" means that it is required to fix in this
PR while "NO" means that it is not required.
# Issue
No issues for this PR.
# Twitter handle
- [hmdev3](https://twitter.com/hmdev3)
# Questions:
1. Is it required to create tests for legacy APIs `LLMChain.run` in the
following scripts?
- libs/langchain/tests/unit_tests/output_parsers/test_fix.py;
- libs/langchain/tests/unit_tests/output_parsers/test_retry.py.
2. Is there a more appropriate expected output type than I expect in the
above table?
- e.g. the `OutputType` of `CombiningOutputParser` should be
SOMETHING...
# Actual outputs (before this PR)
<div id='evidence'></div>
<details><summary>Actual outputs</summary>
## Requirements
- Python==3.9.13
- langchain==0.1.13
```python
Python 3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import langchain
>>> langchain.__version__
'0.1.13'
>>> from langchain import output_parsers
```
### `BooleanOutputParser`
```python
>>> output_parsers.BooleanOutputParser().OutputType
<class 'bool'>
```
### `CombiningOutputParser`
```python
>>> output_parsers.CombiningOutputParser(parsers=[output_parsers.DatetimeOutputParser(), output_parsers.CommaSeparatedListOutputParser()]).OutputType
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType
raise TypeError(
TypeError: Runnable CombiningOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type.
```
### `DatetimeOutputParser`
```python
>>> output_parsers.DatetimeOutputParser().OutputType
<class 'datetime.datetime'>
```
### `EnumOutputParser`
```python
>>> from enum import Enum
>>> class MyEnum(Enum):
... a = 'a'
... b = 'b'
...
>>> output_parsers.EnumOutputParser(enum=MyEnum).OutputType
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType
raise TypeError(
TypeError: Runnable EnumOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type.
```
### `OutputFixingParser`
```python
>>> output_parsers.OutputFixingParser(parser=output_parsers.DatetimeOutputParser()).OutputType
~T
```
### `CommaSeparatedListOutputParser`
```python
>>> output_parsers.CommaSeparatedListOutputParser().OutputType
typing.List[str]
```
### `MarkdownListOutputParser`
```python
>>> output_parsers.MarkdownListOutputParser().OutputType
typing.List[str]
```
### `NumberedListOutputParser`
```python
>>> output_parsers.NumberedListOutputParser().OutputType
typing.List[str]
```
### `JsonOutputKeyToolsParser`
```python
>>> output_parsers.JsonOutputKeyToolsParser(key_name='tool').OutputType
typing.Any
```
### `JsonOutputToolsParser`
```python
>>> output_parsers.JsonOutputToolsParser().OutputType
typing.Any
```
### `PydanticToolsParser`
```python
>>> from langchain.pydantic_v1 import BaseModel
>>> class MyModel(BaseModel):
... a: int
...
>>> output_parsers.PydanticToolsParser(tools=[MyModel, MyModel]).OutputType
typing.Any
```
### `PandasDataFrameOutputParser`
```python
>>> output_parsers.PandasDataFrameOutputParser().OutputType
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType
raise TypeError(
TypeError: Runnable PandasDataFrameOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type.
```
### `PydanticOutputParser`
```python
>>> output_parsers.PydanticOutputParser(pydantic_object=MyModel).OutputType
<class '__main__.MyModel'>
```
### `RegexParser`
```python
>>> output_parsers.RegexParser(regex='$', output_keys=['a']).OutputType
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType
raise TypeError(
TypeError: Runnable RegexParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type.
```
### `RegexDictParser`
```python
>>> output_parsers.RegexDictParser(output_key_to_format={'a':'a'}).OutputType
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType
raise TypeError(
TypeError: Runnable RegexDictParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type.
```
### `RetryOutputParser`
```python
>>> output_parsers.RetryOutputParser(parser=output_parsers.DatetimeOutputParser()).OutputType
~T
```
### `RetryWithErrorOutputParser`
```python
>>> output_parsers.RetryWithErrorOutputParser(parser=output_parsers.DatetimeOutputParser()).OutputType
~T
```
### `StructuredOutputParser`
```python
>>> from langchain.output_parsers.structured import ResponseSchema
>>> response_schemas = [ResponseSchema(name="foo",description="a list of strings",type="List[string]"),ResponseSchema(name="bar",description="a string",type="string"), ]
>>> output_parsers.StructuredOutputParser.from_response_schemas(response_schemas).OutputType
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\venv\lib\site-packages\langchain_core\output_parsers\base.py", line 160, in OutputType
raise TypeError(
TypeError: Runnable StructuredOutputParser doesn't have an inferable OutputType. Override the OutputType property to specify the output type.
```
### `YamlOutputParser`
```python
>>> output_parsers.YamlOutputParser(pydantic_object=MyModel).OutputType
~T
```
<div>
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This change adds args_schema (pydantic BaseModel) to SearxSearchRun for
correct schema formatting on LLM function calls
Issue: currently using SearxSearchRun with OpenAI function calling
returns the following error "TypeError: SearxSearchRun._run() got an
unexpected keyword argument '__arg1' ".
This happens because the schema sent to the LLM is "input:
'{"__arg1":"foobar"}'" while the method should be called with the
"query" parameter.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Updated
*community.langchain_community.document_loaders.directory.py* to enable
the use of multiple glob patterns in the `DirectoryLoader` class. Now,
the glob parameter is of type `list[str] | str` and still defaults to
the same value as before. I updated the docstring of the class to
reflect this, and added a unit test to
*community.tests.unit_tests.document_loaders.test_directory.py* named
`test_directory_loader_glob_multiple`. This test also shows an example
of how to use the new functionality.
- ~~Issue:~~**Discussion Thread:**
https://github.com/langchain-ai/langchain/discussions/18559
- **Dependencies:** None
- **Twitter handle:** N/a
- [x] **Add tests and docs**
- Added test (described above)
- Updated class docstring
- [x] **Lint and test**
---------
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Fix https://github.com/langchain-ai/langchain/issues/22972.
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
```SemanticChunker``` currently provide three methods to split the texts semantically:
- percentile
- standard_deviation
- interquartile
I propose new method ```gradient```. In this method, the gradient of distance is used to split chunks along with the percentile method (technically) . This method is useful when chunks are highly correlated with each other or specific to a domain e.g. legal or medical. The idea is to apply anomaly detection on gradient array so that the distribution become wider and easy to identify boundaries in highly semantic data.
I have tested this merge on a set of 10 domain specific documents (mostly legal).
Details :
- **Issue:** Improvement
- **Dependencies:** NA
- **Twitter handle:** [x.com/prajapat_ravi](https://x.com/prajapat_ravi)
@hwchase17
---------
Co-authored-by: Raviraj Prajapat <raviraj.prajapat@sirionlabs.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Add chat history store based on Kafka.
Files added:
`libs/community/langchain_community/chat_message_histories/kafka.py`
`docs/docs/integrations/memory/kafka_chat_message_history.ipynb`
New issue to be created for future improvement:
1. Async method implementation.
2. Message retrieval based on timestamp.
3. Support for other configs when connecting to cloud hosted Kafka (e.g.
add `api_key` field)
4. Improve unit testing & integration testing.
**Description:**
- What I changed
- By specifying the `id_key` during the initialization of
`EnsembleRetriever`, it is now possible to determine which documents to
merge scores for based on the value corresponding to the `id_key`
element in the metadata, instead of `page_content`. Below is an example
of how to use the modified `EnsembleRetriever`:
```python
retriever = EnsembleRetriever(retrievers=[ret1, ret2], id_key="id") #
The Document returned by each retriever must keep the "id" key in its
metadata.
```
- Additionally, I added a script to easily test the behavior of the
`invoke` method of the modified `EnsembleRetriever`.
- Why I changed
- There are cases where you may want to calculate scores by treating
Documents with different `page_content` as the same when using
`EnsembleRetriever`. For example, when you want to ensemble the search
results of the same document described in two different languages.
- The previous `EnsembleRetriever` used `page_content` as the basis for
score aggregation, making the above usage difficult. Therefore, the
score is now calculated based on the specified key value in the
Document's metadata.
**Twitter handle:** @shimajiroxyz
- **Description:** add tool_messages_formatter for tool calling agent,
make tool messages can be formatted in different ways for your LLM.
- **Issue:** N/A
- **Dependencies:** N/A
**Standardizing DocumentLoader docstrings (of which there are many)**
This PR addresses issue #22866 and adds docstrings according to the
issue's specified format (in the appendix) for files csv_loader.py and
json_loader.py in langchain_community.document_loaders. In particular,
the following sections have been added to both CSVLoader and JSONLoader:
Setup, Instantiate, Load, Async load, and Lazy load. It may be worth
adding a 'Metadata' section to the JSONLoader docstring to clarify how
we want to extract the JSON metadata (using the `metadata_func`
argument). The files I used to walkthrough the various sections were
`example_2.json` from
[HERE](https://support.oneskyapp.com/hc/en-us/articles/208047697-JSON-sample-files)
and `hw_200.csv` from
[HERE](https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html).
---------
Co-authored-by: lucast2021 <lucast2021@headroyce.org>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
- **Description:** A very small fix in the Docstring of
`DuckDuckGoSearchResults` identified in the following issue.
- **Issue:** #22961
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **PR title**: "community: Fix#22975 (Add SSL Verification Option to
Requests Class in langchain_community)"
- **PR message**:
- **Description:**
- Added an optional verify parameter to the Requests class with a
default value of True.
- Modified the get, post, patch, put, and delete methods to include the
verify parameter.
- Updated the _arequest async context manager to include the verify
parameter.
- Added the verify parameter to the GenericRequestsWrapper class and
passed it to the Requests class.
- **Issue:** This PR fixes issue #22975.
- **Dependencies:** No additional dependencies are required for this
change.
- **Twitter handle:** @lunara_x
You can check this change with below code.
```python
from langchain_openai.chat_models import ChatOpenAI
from langchain.requests import RequestsWrapper
from langchain_community.agent_toolkits.openapi import planner
from langchain_community.agent_toolkits.openapi.spec import reduce_openapi_spec
with open("swagger.yaml") as f:
data = yaml.load(f, Loader=yaml.FullLoader)
swagger_api_spec = reduce_openapi_spec(data)
llm = ChatOpenAI(model='gpt-4o')
swagger_requests_wrapper = RequestsWrapper(verify=False) # modified point
superset_agent = planner.create_openapi_agent(swagger_api_spec, swagger_requests_wrapper, llm, allow_dangerous_requests=True, handle_parsing_errors=True)
superset_agent.run(
"Tell me the number and types of charts and dashboards available."
)
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** The PR #22777 introduced a bug in
`_similarity_search_without_score` which was raising the
`OperationFailure` error. The mistake was syntax error for MongoDB
pipeline which has been corrected now.
- **Issue:** #22770
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Thank you for contributing to LangChain!
- [x] **PR title**: "community: OCI GenAI embedding batch size"
- [x] **PR message**:
- **Issue:** #22985
- [ ] **Add tests and docs**: N/A
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Signed-off-by: Anders Swanson <anders.swanson@oracle.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- StopIteration can't be set on an asyncio.Future it raises a TypeError
and leaves the Future pending forever so we need to convert it to a
RuntimeError
- Refactor standard test classes to make them easier to configure
- Update openai to support stop_sequences init param
- Update groq to support stop_sequences init param
- Update fireworks to support max_retries init param
- Update ChatModel.bind_tools to type tool_choice
- Update groq to handle tool_choice="any". **this may be controversial**
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Langchain is very popular among developers in China, but there are still
no good Chinese books or documents, so I want to add my own Chinese
resources on langchain topics, hoping to give Chinese readers a better
experience using langchain. This is not a translation of the official
langchain documentation, but my understanding.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Support batch size**
Baichuan updates the document, indicating that up to 16 documents can be
imported at a time
- **Standardized model init arg names**
- baichuan_api_key -> api_key
- model_name -> model
Here we add `stream_usage` to ChatOpenAI as:
1. a boolean attribute
2. a kwarg to _stream and _astream.
Question: should the `stream_usage` attribute be `bool`, or `bool |
None`?
Currently I've kept it `bool` and defaulted to False. It was implemented
on
[ChatAnthropic](e832bbb486/libs/partners/anthropic/langchain_anthropic/chat_models.py (L535))
as a bool. However, to maintain support for users who access the
behavior via OpenAI's `stream_options` param, this ends up being
possible:
```python
llm = ChatOpenAI(model_kwargs={"stream_options": {"include_usage": True}})
assert not llm.stream_usage
```
(and this model will stream token usage).
Some options for this:
- it's ok
- make the `stream_usage` attribute bool or None
- make an \_\_init\_\_ for ChatOpenAI, set a `._stream_usage` attribute
and read `.stream_usage` from a property
Open to other ideas as well.
**Description:** This PR adds a chat model integration for [Snowflake
Cortex](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions),
which gives an instant access to industry-leading large language models
(LLMs) trained by researchers at companies like Mistral, Reka, Meta, and
Google, including [Snowflake
Arctic](https://www.snowflake.com/en/data-cloud/arctic/), an open
enterprise-grade model developed by Snowflake.
**Dependencies:** Snowflake's
[snowpark](https://pypi.org/project/snowflake-snowpark-python/) library
is required for using this integration.
**Twitter handle:** [@gethouseware](https://twitter.com/gethouseware)
- [x] **Add tests and docs**:
1. integration tests:
`libs/community/tests/integration_tests/chat_models/test_snowflake.py`
2. unit tests:
`libs/community/tests/unit_tests/chat_models/test_snowflake.py`
3. example notebook: `docs/docs/integrations/chat/snowflake.ipynb`
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Adds `response_metadata` to stream responses from OpenAI. This is
returned with `invoke` normally, but wasn't implemented for `stream`.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
While `YouRetriever` supports both You.com's Search and News APIs, news
is supported as an afterthought.
More specifically, not all of the News API parameters are exposed for
the user, only those that happen to overlap with the Search API.
This PR:
- improves support for both APIs, exposing the remaining News API
parameters while retaining backward compatibility
- refactor some REST parameter generation logic
- updates the docstring of `YouSearchAPIWrapper`
- add input validation and warnings to ensure parameters are properly
set by user
- 🚨 Breaking: Limit the news results to `k` items
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Ollama has a raw option now.
https://github.com/ollama/ollama/blob/main/docs/api.md
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
**Issue:**
When using the similarity_search_with_score function in
ElasticsearchStore, I expected to pass in the query_vector that I have
already obtained. I noticed that the _search function does support the
query_vector parameter, but it seems to be ineffective. I am attempting
to resolve this issue.
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Update former pull request:
https://github.com/langchain-ai/langchain/pull/22654.
Modified `langchain_text_splitters.HTMLSectionSplitter`, where in the
latest version `dict` data structure is used to store sections from a
html document, in function `split_html_by_headers`. The header/section
element names serve as dict keys. This can be a problem when duplicate
header/section element names are present in a single html document.
Latter ones can replace former ones with the same name. Therefore some
contents can be miss after html text splitting is conducted.
Using a list to store sections can hopefully solve the problem. A Unit
test considering duplicate header names has been added.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
The generated relationships in the graph had no properties, but the
Relationship class was properly defined with properties. This made it
very difficult to transform conditional sentences into a graph. Adding
properties to relationships can solve this issue elegantly.
The changes expand on the existing LLMGraphTransformer implementation
but add the possibility to define allowed relationship properties like
this: LLMGraphTransformer(llm=llm, relationship_properties=["Condition",
"Time"],)
- **Issue:**
no issue found
- **Dependencies:**
n/a
- **Twitter handle:**
@IstvanSpace
-Quick Test
=================================================================
from dotenv import load_dotenv
import os
from langchain_community.graphs import Neo4jGraph
from langchain_experimental.graph_transformers import
LLMGraphTransformer
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.documents import Document
load_dotenv()
os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI")
os.environ["NEO4J_USERNAME"] = os.getenv("NEO4J_USERNAME")
os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD")
graph = Neo4jGraph()
llm = ChatOpenAI(temperature=0, model_name="gpt-4o")
llm_transformer = LLMGraphTransformer(llm=llm)
#text = "Harry potter likes pies, but only if it rains outside"
text = "Jack has a dog named Max. Jack only walks Max if it is sunny
outside."
documents = [Document(page_content=text)]
llm_transformer_props = LLMGraphTransformer(
llm=llm,
relationship_properties=["Condition"],
)
graph_documents_props =
llm_transformer_props.convert_to_graph_documents(documents)
print(f"Nodes:{graph_documents_props[0].nodes}")
print(f"Relationships:{graph_documents_props[0].relationships}")
graph.add_graph_documents(graph_documents_props)
---------
Co-authored-by: Istvan Lorincz <istvan.lorincz@pm.me>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Add admonition to the documentation to make sure users are aware that
the tool allows execution of code on the host machine using a python
interpreter (by design).
If the global `debug` flag is enabled, the agent will get the following
error in `FunctionCallbackHandler._on_tool_end` at runtime.
```
Error in ConsoleCallbackHandler.on_tool_end callback: AttributeError("'list' object has no attribute 'strip'")
```
By calling str() before strip(), the error was avoided.
This error can be seen at
[debugging.ipynb](https://github.com/langchain-ai/langchain/blob/master/docs/docs/how_to/debugging.ipynb).
- Issue: NA
- Dependencies: NA
- Twitter handle: https://x.com/kiarina37
Remove the REPL from community, and suggest an alternative import from
langchain_experimental.
Fix for this issue:
https://github.com/langchain-ai/langchain/issues/14345
This is not a bug in the code or an actual security risk. The python
REPL itself is behaving as expected.
The PR is done to appease blanket security policies that are just
looking for the presence of exec in the code.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR moves the validation of the decorator to a better place to avoid
creating bugs while deprecating code.
Prevent issues like this from arising:
https://github.com/langchain-ai/langchain/issues/22510
we should replace with a linter at some point that just does static
analysis
Preserves string content chunks for non tool call requests for
convenience.
One thing - Anthropic events look like this:
```
RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start')
RawContentBlockDeltaEvent(delta=TextDelta(text='<thinking>\nThe', type='text_delta'), index=0, type='content_block_delta')
RawContentBlockDeltaEvent(delta=TextDelta(text=' provide', type='text_delta'), index=0, type='content_block_delta')
...
RawContentBlockStartEvent(content_block=ToolUseBlock(id='toolu_01GJ6x2ddcMG3psDNNe4eDqb', input={}, name='get_weather', type='tool_use'), index=1, type='content_block_start')
RawContentBlockDeltaEvent(delta=InputJsonDelta(partial_json='', type='input_json_delta'), index=1, type='content_block_delta')
```
Note that `delta` has a `type` field. With this implementation, I'm
dropping it because `merge_list` behavior will concatenate strings.
We currently have `index` as a special field when merging lists, would
it be worth adding `type` too?
If so, what do we set as a context block chunk? `text` vs.
`text_delta`/`tool_use` vs `input_json_delta`?
CC @ccurme @efriis @baskaryan
- **Description:** Some of the Cross-Encoder models provide scores in
pairs, i.e., <not-relevant score (higher means the document is less
relevant to the query), relevant score (higher means the document is
more relevant to the query)>. However, the `HuggingFaceCrossEncoder`
`score` method does not currently take into account the pair situation.
This PR addresses this issue by modifying the method to consider only
the relevant score if score is being provided in pair. The reason for
focusing on the relevant score is that the compressors select the top-n
documents based on relevance.
- **Issue:** #22556
- Please also refer to this
[comment](https://github.com/UKPLab/sentence-transformers/issues/568#issuecomment-729153075)
- **PR title**: [community] add chat model llamacpp
- **PR message**:
- **Description:** This PR introduces a new chat model integration with
llamacpp_python, designed to work similarly to the existing ChatOpenAI
model.
+ Work well with instructed chat, chain and function/tool calling.
+ Work with LangGraph (persistent memory, tool calling), will update
soon
- **Dependencies:** This change requires the llamacpp_python library to
be installed.
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Updated ChatGroq doc string as per issue
https://github.com/langchain-ai/langchain/issues/22296:"langchain_groq:
updated docstring for ChatGroq in langchain_groq to match that of the
description (in the appendix) provided in issue
https://github.com/langchain-ai/langchain/issues/22296. "
Issue: This PR is in response to issue
https://github.com/langchain-ai/langchain/issues/22296, and more
specifically the ChatGroq model. In particular, this PR updates the
docstring for langchain/libs/partners/groq/langchain_groq/chat_model.py
by adding the following sections: Instantiate, Invoke, Stream, Async,
Tool calling, Structured Output, and Response metadata. I used the
template from the Anthropic implementation and referenced the Appendix
of the original issue post. I also noted that: `usage_metadata `returns
none for all ChatGroq models I tested; there is no mention of image
input in the ChatGroq documentation; unlike that of ChatHuggingFace,
`.stream(messages)` for ChatGroq returned blocks of output.
---------
Co-authored-by: lucast2021 <lucast2021@headroyce.org>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds the feature add Prem Template feature in ChatPremAI.
Additionally it fixes a minor bug for API auth error when API passed
through arguments.
Description: Adjusting the syntax for creating the vectorstore
collection (in the case of automatic embedding computation) for the most
idiomatic way to submit the stored secret name.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:**
Update the NVIDIA Riva tool documentation to use NVIDIA NIM for the LLM.
Show how to use NVIDIA NIMs and link to documentation for LangChain with
NIM.
---------
Co-authored-by: Hayden Wolff <hwolff@nvidia.com>
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
This PR addresses several lint errors in the core package of LangChain.
Specifically, the following issues were fixed:
1.Unexpected keyword argument "required" for "Field" [call-arg]
2.tests/integration_tests/chains/test_cpal.py:263: error: Unexpected
keyword argument "narrative_input" for "QueryModel" [call-arg]
This should make it obvious that a few of the agents in langchain
experimental rely on the python REPL as a tool under the hood, and will
force users to opt-in.
This downgrades `Function/tool calling` from a h3 to an h4 which means
it'll no longer show up in the right sidebar, but any direct links will
still work. I think that is ok, but LMK if you disapprove.
CC @hwchase17 @eyurtsev @rlancemartin
We need to use a different version of numpy for py3.8 and py3.12 in
pyproject.
And so do projects that use that Python version range and import
langchain.
- **Twitter handle:** _cbornet
**Description**
sqlalchemy uses "sqlalchemy.engine.URL" type for db uri argument.
Added 'URL' type for compatibility.
**Issue**: None
**Dependencies:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This implements `show_progress` more consistently
(i.e. it is also added to the `HuggingFaceBgeEmbeddings` object).
- **Issue:** This implements `show_progress` more consistently in the
embeddings huggingface classes. Previously this could have been set via
`encode_kwargs`.
- **Dependencies:** None
- **Twitter handle:** @jonzeolla
… (#22795)
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:** This PR updates the documentation to reflect the recent
code changes.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** A change I submitted recently introduced a bug in
`YoutubeLoader`'s `LINES` output format. In those conditions, curly
braces ("`{}`") creates a set, not a dictionary. This bugfix explicitly
specifies that a dictionary is created.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter:** lsloan_umich
- **Mastodon:**
[lsloan@mastodon.social](https://mastodon.social/@lsloan)
changed "# 🌟Recognition" to "### 🌟 Recognition" to match the rest of the
subheadings.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
Support for old clients (Thin and Thick) Oracle Vector Store
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
Support for old clients (Thin and Thick) Oracle Vector Store
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
Have our own local tests
---------
Co-authored-by: rohan.aggarwal@oracle.com <rohaagga@phoenix95642.dev3sub2phx.databasede3phx.oraclevcn.com>
- **Description:** Add a new format, `CHUNKS`, to
`langchain_community.document_loaders.youtube.YoutubeLoader` which
creates multiple `Document` objects from YouTube video transcripts
(captions), each of a fixed duration. The metadata of each chunk
`Document` includes the start time of each one and a URL to that time in
the video on the YouTube website.
I had implemented this for UMich (@umich-its-ai) in a local module, but
it makes sense to contribute this to LangChain community for all to
benefit and to simplify maintenance.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter:** lsloan_umich
- **Mastodon:**
[lsloan@mastodon.social](https://mastodon.social/@lsloan)
With regards to **tests and documentation**, most existing features of
the `YoutubeLoader` class are not tested. Only the
`YoutubeLoader.extract_video_id()` static method had a test. However,
while I was waiting for this PR to be reviewed and merged, I had time to
add a test for the chunking feature I've proposed in this PR.
I have added an example of using chunking to the
`docs/docs/integrations/document_loaders/youtube_transcript.ipynb`
notebook.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR add supports for Azure Cosmos DB for NoSQL vector store.
Summary:
Description: added vector store integration for Azure Cosmos DB for
NoSQL Vector Store,
Dependencies: azure-cosmos dependency,
Tag maintainer: @hwchase17, @baskaryan @efriis @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** As pointed out in this issue #22770, DocumentDB
`similarity_search` does not support filtering through metadata which
this PR adds by passing in the parameter `filter`. Also this PR fixes a
minor Documentation error.
- **Issue:** #22770
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:** Ollama vision with messages in OpenAI-style support `{
"image_url": { "url": ... } }`
**Issue:** #22460
Added flexible solution for ChatOllama to support chat messages with
images. Works when you provide either `image_url` as a string or as a
dict with "url" inside (like OpenAI does). So it makes available to use
tuples with `ChatPromptTemplate.from_messages()`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "langchain: Fix chain_filter.py to be compatible
with async"
- [ ] **PR message**:
- **Description:** chain_filter is not compatible with async.
- **Twitter handle:** pprados
- [X ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
---------
Signed-off-by: zhangwangda <zhangwangda94@163.com>
Co-authored-by: Prakul <discover.prakul@gmail.com>
Co-authored-by: Lei Zhang <zhanglei@apache.org>
Co-authored-by: Gin <ictgtvt@gmail.com>
Co-authored-by: wangda <38549158+daziz@users.noreply.github.com>
Co-authored-by: Max Mulatz <klappradla@posteo.net>
Thank you for contributing to LangChain!
### Description
Fix the example in the docstring of redis store.
Change the initilization logic and remove redundant check, enhance error
message.
### Issue
The example in docstring of how to use redis store was wrong.
### Dependencies
Nothing
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- [ ] **Miscellaneous updates and fixes**:
- **Description:** Handled error in querying; quotes in table names;
updated gpudb API
- **Issue:** Threw an error with an error message difficult to
understand if a query failed or returned no records
- **Dependencies:** Updated GPUDB API version to `7.2.0.9`
@baskaryan @hwchase17
- **Description:** allow to use partial variables to pass `top_k` and
`table_info`
- **Issue:** no
- **Dependencies:** no
- **Twitter handle:** @gymnstcs
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This PR updates the `WandbTracer` to work with the
new RunV2 API so that wandb Traces logging works correctly for new
LangChain versions. Here's an example
[run](https://wandb.ai/parambharat/langchain-tracing/runs/wpm99ftq) from
the existing tests
- **Issue:** https://github.com/wandb/wandb/issues/7762
- **Twitter handle:** @ParamBharat
_If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17._
**Updated ChatHuggingFace doc string as per issue #22296**:
"langchain_huggingface: updated docstring for ChatHuggingFace in
langchain_huggingface to match that of the description (in the appendix)
provided in issue #22296. "
**Issue:** This PR is in response to issue #22296, and more specifically
ChatHuggingFace model. In particular, this PR updates the docstring for
langchain/libs/partners/hugging_face/langchain_huggingface/chat_models/huggingface.py
by adding the following sections: Instantiate, Invoke, Stream, Async,
Tool calling, and Response metadata. I used the template from the
Anthropic implementation and referenced the Appendix of the original
issue post. I also noted that: langchain_community hugging face llms do
not work with langchain_huggingface's ChatHuggingFace model (at least
for me); the .stream(messages) functionality of ChatHuggingFace only
returned a block of response.
---------
Co-authored-by: lucast2021 <lucast2021@headroyce.org>
Co-authored-by: Bagatur <baskaryan@gmail.com>
LLMs struggle with Graph RAG, because it's different from vector RAG in
a way that you don't provide the whole context, only the answer and the
LLM has to believe. However, that doesn't really work a lot of the time.
However, if you wrap the context as function response the accuracy is
much better.
btw... `union[LLMChain, Runnable]` is linting fun, that's why so many
ignores
**Description:** this PR adds Volcengine Rerank capability to Langchain,
you can find Volcengine Rerank API from
[here](https://www.volcengine.com/docs/84313/1254474) &
[here](https://www.volcengine.com/docs/84313/1254605).
[Volcengine](https://www.volcengine.com/) is a cloud service platform
developed by ByteDance, the parent company of TikTok. You can obtain
Volcengine API AK/SK from
[here](https://www.volcengine.com/docs/84313/1254553).
**Dependencies:** VolcengineRerank depends on `volcengine` python
package.
**Twitter handle:** my twitter/x account is https://x.com/LastMonopoly
and I'd like a mention, thank you!
**Tests and docs**
1. integration test: `test_volcengine_rerank.py`
2. example notebook: `volcengine_rerank.ipynb`
**Lint and test**: I have run `make format`, `make lint` and `make test`
from the root of the package I've modified.
Hi 👋
First off, thanks a ton for your work on this 💚 Really appreciate what
you're providing here for the community.
## Description
This PR adds a basic language parser for the
[Elixir](https://elixir-lang.org/) programming language. The parser code
is based upon the approach outlined in
https://github.com/langchain-ai/langchain/pull/13318: it's using
`tree-sitter` under the hood and aligns with all the other `tree-sitter`
based parses added that PR.
The `CHUNK_QUERY` I'm using here is probably not the most sophisticated
one, but it worked for my application. It's a starting point to provide
"core" parsing support for Elixir in LangChain. It enables people to use
the language parser out in real world applications which may then lead
to further tweaking of the queries. I consider this PR just the ground
work.
- **Dependencies:** requires `tree-sitter` and `tree-sitter-languages`
from the extended dependencies
- **Twitter handle:**`@bitcrowd`
## Checklist
- [x] **PR title**: "package: description"
- [x] **Add tests and docs**
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
<!-- If no one reviews your PR within a few days, please @-mention one
of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. -->
The fact that we outsourced pgvector to another project has an
unintended effect. The mapping dictionary found by
`_get_builtin_translator()` cannot recognize the new version of pgvector
because it comes from another package.
`SelfQueryRetriever` no longer knows `PGVector`.
I propose to fix this by creating a global dictionary that can be
populated by various database implementations. Thus, importing
`langchain_postgres` will allow the registration of the `PGvector`
mapping.
But for the moment I'm just adding a lazy import
Furthermore, the implementation of _get_builtin_translator()
reconstructs the BUILTIN_TRANSLATORS variable with each invocation,
which is not very efficient. A global map would be an optimization.
- **Twitter handle:** pprados
@eyurtsev, can you review this PR? And unlock the PR [Add async mode for
pgvector](https://github.com/langchain-ai/langchain-postgres/pull/32)
and PR [community[minor]: Add SQL storage
implementation](https://github.com/langchain-ai/langchain/pull/22207)?
Are you in favour of a global dictionary-based implementation of
Translator?
## Description
This PR addresses a logging inconsistency in the `get_user_agent`
function. Previously, the function was using the root logger to log a
warning message when the "USER_AGENT" environment variable was not set.
This bypassed the custom logger `log` that was created at the start of
the module, leading to potential inconsistencies in logging behavior.
Changes:
- Replaced `logging.warning` with `log.warning` in the `get_user_agent`
function to ensure that the custom logger is used.
This change ensures that all logging in the `get_user_agent` function
respects the configurations of the custom logger, leading to more
consistent and predictable logging behavior.
## Dependencies
None
## Issue
None
## Tests and docs
☝🏻 see description
## `make format`, `make lint` & `cd libs/community; make test`
```shell
> make format
poetry run ruff format docs templates cookbook
1417 files left unchanged
poetry run ruff check --select I --fix docs templates cookbook
All checks passed!
```
```shell
> make lint
poetry run ruff check docs templates cookbook
All checks passed!
poetry run ruff format docs templates cookbook --diff
1417 files already formatted
poetry run ruff check --select I docs templates cookbook
All checks passed!
git grep 'from langchain import' docs/docs templates cookbook | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
```
~cd libs/community; make test~ too much dependencies for integration ...
```shell
> poetry run pytest tests/unit_tests
....
==== 884 passed, 466 skipped, 4447 warnings in 15.93s ====
```
I choose you randomly : @ccurme
Adding `UpstashRatelimitHandler` callback for rate limiting based on
number of chain invocations or LLM token usage.
For more details, see [upstash/ratelimit-py
repository](https://github.com/upstash/ratelimit-py) or the notebook
guide included in this PR.
Twitter handle: @cahidarda
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Refactor streaming to use raw events;
- Add `stream_usage` class attribute and kwarg to stream methods that,
if True, will include separate chunks in the stream containing usage
metadata.
There are two ways to implement streaming with anthropic's python sdk.
They have slight differences in how they surface usage metadata.
1. [Use helper
functions](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-helpers).
This is what we are doing now.
```python
count = 1
with client.messages.stream(**params) as stream:
for text in stream.text_stream:
snapshot = stream.current_message_snapshot
print(f"{count}: {snapshot.usage} -- {text}")
count = count + 1
final_snapshot = stream.get_final_message()
print(f"{count}: {final_snapshot.usage}")
```
```
1: Usage(input_tokens=8, output_tokens=1) -- Hello
2: Usage(input_tokens=8, output_tokens=1) -- !
3: Usage(input_tokens=8, output_tokens=1) -- How
4: Usage(input_tokens=8, output_tokens=1) -- can
5: Usage(input_tokens=8, output_tokens=1) -- I
6: Usage(input_tokens=8, output_tokens=1) -- assist
7: Usage(input_tokens=8, output_tokens=1) -- you
8: Usage(input_tokens=8, output_tokens=1) -- today
9: Usage(input_tokens=8, output_tokens=1) -- ?
10: Usage(input_tokens=8, output_tokens=12)
```
To do this correctly, we need to emit a new chunk at the end of the
stream containing the usage metadata.
2. [Handle raw
events](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-responses)
```python
stream = client.messages.create(**params, stream=True)
count = 1
for event in stream:
print(f"{count}: {event}")
count = count + 1
```
```
1: RawMessageStartEvent(message=Message(id='msg_01Vdyov2kADZTXqSKkfNJXcS', content=[], model='claude-3-haiku-20240307', role='assistant', stop_reason=None, stop_sequence=None, type='message', usage=Usage(input_tokens=8, output_tokens=1)), type='message_start')
2: RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start')
3: RawContentBlockDeltaEvent(delta=TextDelta(text='Hello', type='text_delta'), index=0, type='content_block_delta')
4: RawContentBlockDeltaEvent(delta=TextDelta(text='!', type='text_delta'), index=0, type='content_block_delta')
5: RawContentBlockDeltaEvent(delta=TextDelta(text=' How', type='text_delta'), index=0, type='content_block_delta')
6: RawContentBlockDeltaEvent(delta=TextDelta(text=' can', type='text_delta'), index=0, type='content_block_delta')
7: RawContentBlockDeltaEvent(delta=TextDelta(text=' I', type='text_delta'), index=0, type='content_block_delta')
8: RawContentBlockDeltaEvent(delta=TextDelta(text=' assist', type='text_delta'), index=0, type='content_block_delta')
9: RawContentBlockDeltaEvent(delta=TextDelta(text=' you', type='text_delta'), index=0, type='content_block_delta')
10: RawContentBlockDeltaEvent(delta=TextDelta(text=' today', type='text_delta'), index=0, type='content_block_delta')
11: RawContentBlockDeltaEvent(delta=TextDelta(text='?', type='text_delta'), index=0, type='content_block_delta')
12: RawContentBlockStopEvent(index=0, type='content_block_stop')
13: RawMessageDeltaEvent(delta=Delta(stop_reason='end_turn', stop_sequence=None), type='message_delta', usage=MessageDeltaUsage(output_tokens=12))
14: RawMessageStopEvent(type='message_stop')
```
Here we implement the second option, in part because it should make
things easier when implementing streaming tool calls in the near future.
This would add two new chunks to the stream-- one at the beginning and
one at the end-- with blank content and containing usage metadata. We
add kwargs to the stream methods and a class attribute allowing for this
behavior to be toggled. I enabled it by default. If we merge this we can
add the same kwargs / attribute to OpenAI.
Usage:
```python
from langchain_anthropic import ChatAnthropic
model = ChatAnthropic(
model="claude-3-haiku-20240307",
temperature=0
)
full = None
for chunk in model.stream("hi"):
full = chunk if full is None else full + chunk
print(chunk)
print(f"\nFull: {full}")
```
```
content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 0, 'total_tokens': 8}
content='Hello' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='!' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' How' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' can' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' I' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' assist' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' you' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' today' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='?' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12}
Full: content='Hello! How can I assist you today?' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 12, 'total_tokens': 20}
```
They cause `poetry lock` to take a ton of time, and `uv pip install` can
resolve the constraints from these toml files in trivial time
(addressing problem with #19153)
This allows us to properly upgrade lockfile dependencies moving forward,
which revealed some issues that were either fixed or type-ignored (see
file comments)
Corrected a typo in the AutoGPT example notebook. Changed "Needed synce
jupyter runs an async eventloop" to "Needed since Jupyter runs an async
event loop".
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
removed an extra space before the period in the "Click **Create
codespace on master**." line.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- [x] **Adding AsyncRootListener**: "langchain_core: Adding
AsyncRootListener"
- **Description:** Adding an AsyncBaseTracer, AsyncRootListener and
`with_alistener` function. This is to enable binding async root listener
to runnables. This currently only supported for sync listeners.
- **Issue:** None
- **Dependencies:** None
- [x] **Add tests and docs**: Added units tests and example snippet code
within the function description of `with_alistener`
- [x] **Lint and test**: Run make format_diff, make lint_diff and make
test
## Description
The `path` param is used to specify the local persistence directory,
which isn't required if using Qdrant server.
This is a breaking but necessary change.
This PR adds support for using Databricks Unity Catalog functions as
LangChain tools, which runs inside a Databricks SQL warehouse.
* An example notebook is provided.
The response.get("model", self.model_name) checks if the model key
exists in the response dictionary. If it does, it uses that value;
otherwise, it uses self.model_name.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**:
- **Description:** This PR corrects the return type in the docstring of
the `docs/api_reference/create_api_rst.py/_load_package_modules`
function. The return type was previously described as a list of
Co-authored-by: suganthsolamanraja <suganth.solamanraja@techjays..com>
langchain-together depends on langchain-openai ^0.1.8
langchain-openai 0.1.8 has langchain-core >= 0.2.2
Here we bump langchain-core to 0.2.2, just to pass minimum dependency
version tests.
Corrected the phrase "complete done" to "completely done" for better
grammatical accuracy and clarity in the Agents section of the README.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
More direct entrypoint for a common use-case. Meant to give people a
more hands-on intro to document loaders/loading data from different data
sources as well.
Some duplicate content for RAG and extraction (to show what you can do
with the loaded documents), but defers to the appropriate sections
rather than going too in-depth.
@baskaryan @hwchase17
decisions to discuss
- only chat models
- model_provider isn't based on any existing values like llm-type,
package names, class names
- implemented as function not as a wrapper ChatModel
- function name (init_model)
- in langchain as opposed to community or core
- marked beta
Thank you for contributing to LangChain!
**Description:** Adds Langchain support for Nomic Embed Vision
**Twitter handle:** nomic_ai,zach_nussbaum
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This PR addresses an issue with an existing test that
was not effectively testing the intended functionality. The previous
test setup did not adequately validate the filtering of the labels in
neo4j, because the nodes and relationship in the test data did not have
any properties set. Without properties these labels would not have been
returned, regardless of the filtering.
---------
Co-authored-by: Oskar Hane <oh@oskarhane.com>
This PR adds a constructor `metadata_indexing` parameter to the
Cassandra vector store to allow optional fine-tuning of which fields of
the metadata are to be indexed.
This is a feature supported by the underlying CassIO library. Indexing
mode of "all", "none" or deny- and allow-list based choices are
available.
The rationale is, in some cases it's advisable to programmatically
exclude some portions of the metadata from the index if one knows in
advance they won't ever be used at search-time. this keeps the index
more lightweight and performant and avoids limitations on the length of
_indexed_ strings.
I added a integration test of the feature. I also added the possibility
of running the integration test with Cassandra on an arbitrary IP
address (e.g. Dockerized), via
`CASSANDRA_CONTACT_POINTS=10.1.1.5,10.1.1.6 poetry run pytest [...]` or
similar.
While I was at it, I added a line to the `.gitignore` since the mypy
_test_ cache was not ignored yet.
My X (Twitter) handle: @rsprrs.
**Description:** This PR adds a `USER_AGENT` env variable that is to be
used for web scraping. It creates a util to get that user agent and uses
it in the classes used for scraping in [this piece of
doc](https://python.langchain.com/v0.1/docs/use_cases/web_scraping/).
Identifying your scraper is considered a good politeness practice, this
PR aims at easing it.
**Issue:** `None`
**Dependencies:** `None`
**Twitter handle:** `None`
# package community: Fix SQLChatMessageHistory
## Description
Here is a rewrite of `SQLChatMessageHistory` to properly implement the
asynchronous approach. The code circumvents [issue
22021](https://github.com/langchain-ai/langchain/issues/22021) by
accepting a synchronous call to `def add_messages()` in an asynchronous
scenario. This bypasses the bug.
For the same reasons as in [PR
22](https://github.com/langchain-ai/langchain-postgres/pull/32) of
`langchain-postgres`, we use a lazy strategy for table creation. Indeed,
the promise of the constructor cannot be fulfilled without this. It is
not possible to invoke a synchronous call in a constructor. We
compensate for this by waiting for the next asynchronous method call to
create the table.
The goal of the `PostgresChatMessageHistory` class (in
`langchain-postgres`) is, among other things, to be able to recycle
database connections. The implementation of the class is problematic, as
we have demonstrated in [issue
22021](https://github.com/langchain-ai/langchain/issues/22021).
Our new implementation of `SQLChatMessageHistory` achieves this by using
a singleton of type (`Async`)`Engine` for the database connection. The
connection pool is managed by this singleton, and the code is then
reentrant.
We also accept the type `str` (optionally complemented by `async_mode`.
I know you don't like this much, but it's the only way to allow an
asynchronous connection string).
In order to unify the different classes handling database connections,
we have renamed `connection_string` to `connection`, and `Session` to
`session_maker`.
Now, a single transaction is used to add a list of messages. Thus, a
crash during this write operation will not leave the database in an
unstable state with a partially added message list. This makes the code
resilient.
We believe that the `PostgresChatMessageHistory` class is no longer
necessary and can be replaced by:
```
PostgresChatMessageHistory = SQLChatMessageHistory
```
This also fixes the bug.
## Issue
- [issue 22021](https://github.com/langchain-ai/langchain/issues/22021)
- Bug in _exit_history()
- Bugs in PostgresChatMessageHistory and sync usage
- Bugs in PostgresChatMessageHistory and async usage
- [issue
36](https://github.com/langchain-ai/langchain-postgres/issues/36)
## Twitter handle:
pprados
## Tests
- libs/community/tests/unit_tests/chat_message_histories/test_sql.py
(add async test)
@baskaryan, @eyurtsev or @hwchase17 can you check this PR ?
And, I've been waiting a long time for validation from other PRs. Can
you take a look?
- [PR 32](https://github.com/langchain-ai/langchain-postgres/pull/32)
- [PR 15575](https://github.com/langchain-ai/langchain/pull/15575)
- [PR 13200](https://github.com/langchain-ai/langchain/pull/13200)
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** The InMemoryVectorStore is a nice and simple vector
store implementation for quick development and debugging. The current
implementation is quite limited in its functionalities. This PR extends
the functionalities by adding utility function to persist the vector
store to a json file and to load it from a json file. We choose the json
file format because it allows inspection of the database contents in a
text editor, which is great for debugging. Furthermore, it adds a
`filter` keyword that can be used to filter out documents on their
`page_content` or `metadata`.
- **Issue:** -
- **Dependencies:** -
- **Twitter handle:** @Vincent_Min
- [ ] **community**: "vectorstore: added filtering support for LanceDB
vector store"
- [ ] **This PR adds filtering capabilities to LanceDB**:
- **Description:** In LanceDB filtering can be applied when searching
for data into the vectorstore. It is using the SQL language as mentioned
in the LanceDB documentation.
- **Issue:** #18235
- **Dependencies:** No
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
This PR adds deduplication of callback handlers in merge_configs.
Fix for this issue:
https://github.com/langchain-ai/langchain/issues/22227
The issue appears when the code is:
1) running python >=3.11
2) invokes a runnable from within a runnable
3) binds the callbacks to the child runnable from the parent runnable
using with_config
In this case, the same callbacks end up appearing twice: (1) the first
time from with_config, (2) the second time with langchain automatically
propagating them on behalf of the user.
Prior to this PR this will emit duplicate events:
```python
@tool
async def get_items(question: str, callbacks: Callbacks): # <--- Accept callbacks
"""Ask question"""
template = ChatPromptTemplate.from_messages(
[
(
"human",
"'{question}"
)
]
)
chain = template | chat_model.with_config(
{
"callbacks": callbacks, # <-- Propagate callbacks
}
)
return await chain.ainvoke({"question": question})
```
Prior to this PR this will work work correctly (no duplicate events):
```python
@tool
async def get_items(question: str, callbacks: Callbacks): # <--- Accept callbacks
"""Ask question"""
template = ChatPromptTemplate.from_messages(
[
(
"human",
"'{question}"
)
]
)
chain = template | chat_model
return await chain.ainvoke({"question": question}, {"callbacks": callbacks})
```
This will also work (as long as the user is using python >= 3.11) -- as
langchain will automatically propagate callbacks
```python
@tool
async def get_items(question: str,):
"""Ask question"""
template = ChatPromptTemplate.from_messages(
[
(
"human",
"'{question}"
)
]
)
chain = template | chat_model
return await chain.ainvoke({"question": question})
```
Thank you for contributing to LangChain!
**Description:** update to the Vectara / Langchain integration to
integrate new Vectara capabilities:
- Full RAG implemented as a Runnable with as_rag()
- Vectara chat supported with as_chat()
- Both support streaming response
- Updated documentation and example notebook to reflect all the changes
- Updated Vectara templates
**Twitter handle:** ofermend
**Add tests and docs**: no new tests or docs, but updated both existing
tests and existing docs
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**:
- **Description:** Updated dead link referencing chroma docs in Chroma
notebook under vectorstores
…s and Opensearch Semantic Cache
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [ ] **Packages affected**:
- community: fix `cosine_similarity` to support simsimd beyond 3.7.7
- partners/milvus: fix `cosine_similarity` to support simsimd beyond
3.7.7
- partners/mongodb: fix `cosine_similarity` to support simsimd beyond
3.7.7
- partners/pinecone: fix `cosine_similarity` to support simsimd beyond
3.7.7
- partners/qdrant: fix `cosine_similarity` to support simsimd beyond
3.7.7
- [ ] **Broadcast operation failure while using simsimd beyond v3.7.7**:
- **Description:** I was using simsimd 4.3.1 and the unsupported operand
type issue popped up. When I checked out the repo and ran the tests,
they failed as well (have attached a screenshot for that). Looks like it
is a variant of https://github.com/langchain-ai/langchain/issues/18022 .
Prior to 3.7.7, simd.cdist returned an ndarray but now it returns
simsimd.DistancesTensor which is ineligible for a broadcast operation
with numpy. With this change, it also remove the need to explicitly cast
`Z` to numpy array
- **Issue:** #19905
- **Dependencies:** No
- **Twitter handle:** https://x.com/GetzJoydeep
<img width="1622" alt="Screenshot 2024-05-29 at 2 50 00 PM"
src="https://github.com/langchain-ai/langchain/assets/31132555/fb27b383-a9ae-4a6f-b355-6d503b72db56">
- [ ] **Considerations**:
1. I started with community but since similar changes were there in
Milvus, MongoDB, Pinecone, and QDrant so I modified their files as well.
If touching multiple packages in one PR is not the norm, then I can
remove them from this PR and raise separate ones
2. I have run and verified that the tests work. Since, only MongoDB had
tests, I ran theirs and verified it works as well. Screenshots attached
:
<img width="1573" alt="Screenshot 2024-05-29 at 2 52 13 PM"
src="https://github.com/langchain-ai/langchain/assets/31132555/ce87d1ea-19b6-4900-9384-61fbc1a30de9">
<img width="1614" alt="Screenshot 2024-05-29 at 3 33 51 PM"
src="https://github.com/langchain-ai/langchain/assets/31132555/6ce1d679-db4c-4291-8453-01028ab2dca5">
I have added a test for simsimd. I feel it may not go well with the
CI/CD setup as installing simsimd is not a dependency requirement. I
have just imported simsimd to ensure simsimd cosine similarity is
invoked. However, its not a good approach. Suggestions are welcome and I
can make the required changes on the PR. Please provide guidance on the
same as I am new to the community.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
### Description
Add tools implementation to `ChatEdenAI`:
- `bind_tools()`
- `with_structured_output()`
### Documentation
Updated `docs/docs/integrations/chat/edenai.ipynb`
### Notes
We don´t support stream with tools as of yet. If stream is called with
tools we directly yield the whole message from `generate` (implemented
the same way as Anthropic did).
- [x] **PR title**: Update docstrings for OpenAI base.py
-**Description:** Updated the docstring of few OpenAI functions for a
better understanding of the function.
- **Issue:** #21983
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Noticing errors logged in some situations when tracing with Langsmith:
```python
from langchain_core.pydantic_v1 import BaseModel
from langchain_anthropic import ChatAnthropic
class AnswerWithJustification(BaseModel):
"""An answer to the user question along with justification for the answer."""
answer: str
justification: str
llm = ChatAnthropic(model="claude-3-haiku-20240307")
structured_llm = llm.with_structured_output(AnswerWithJustification)
list(structured_llm.stream("What weighs more a pound of bricks or a pound of feathers"))
```
```
Error in LangChainTracer.on_chain_end callback: AttributeError("'NoneType' object has no attribute 'append'")
[AnswerWithJustification(answer='A pound of bricks and a pound of feathers weigh the same amount.', justification='This is because a pound is a unit of mass, not volume. By definition, a pound of any material, whether bricks or feathers, will weigh the same - one pound. The physical size or volume of the materials does not matter when measuring by mass. So a pound of bricks and a pound of feathers both weigh exactly one pound.')]
```
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
The Vectorstore's API `as_retriever` doesn't expose explicitly the
parameters `search_type` and `search_kwargs` and so these are not well
documented.
This PR improves `as_retriever` for the Cassandra VectorStore by making
these parameters explicit.
NB: An alternative would have been to modify `as_retriever` in
`Vectorstore`. But there's probably a good reason these were not exposed
in the first place ? Is it because implementations may decide to not
support them and have fixed values when creating the
VectorStoreRetriever ?
- **Description:** Added support for using HuggingFacePipeline in
ChatHuggingFace (previously it was only usable with API endpoints,
probably by oversight).
- **Issue:** #19997
- **Dependencies:** none
- **Twitter handle:** none
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR introduces namespace support for Upstash Vector Store, which
would allow users to partition their data in the vector index.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:**
This PR fixes a rendering issue in the docs (Python notebook) of HANA
Cloud Vector Engine.
- **Issue:** N/A
- **Dependencies:** no new dependencies added
File of the fixed notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
## Description
This PR allows passing the HTMLSectionSplitter paths to xslt files. It
does so by fixing two trivial bugs with how passed paths were being
handled. It also changes the default value of the param `xslt_path` to
`None` so the special case where the file was part of the langchain
package could be handled.
## Issue
#22175
- [X] **PR title**: "community: added optional params to Airtable
table.all()"
- [X] **PR message**:
- **Description:** Add's **kwargs to AirtableLoader to allow for kwargs:
https://pyairtable.readthedocs.io/en/latest/api.html#pyairtable.Table.all
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** parakoopa88
- [X] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
"community/embeddings: update oracleai.py"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
Adding oracle VECTOR_ARRAY_T support.
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
Tests are not impacted.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Done.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- **Description:** When I was running the SparkLLMTextEmbeddings,
app_id, api_key and api_secret are all correct, but it cannot run
normally using the current URL.
```python
# example
from langchain_community.embeddings import SparkLLMTextEmbeddings
embedding= SparkLLMTextEmbeddings(
spark_app_id="my-app-id",
spark_api_key="my-api-key",
spark_api_secret="my-api-secret"
)
embedding= "hello"
print(spark.embed_query(text1))
```
So I updated the url and request body parameters according to
[Embedding_api](https://www.xfyun.cn/doc/spark/Embedding_api.html), now
it is runnable.
**Description:** [IPEX-LLM](https://github.com/intel-analytics/ipex-llm)
is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local
PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low
latency. This PR adds ipex-llm integrations to langchain for BGE
embedding support on both Intel CPU and GPU.
**Dependencies:** `ipex-llm`, `sentence-transformers`
**Contribution maintainer**: @Oscilloscope98
**tests and docs**:
- langchain/docs/docs/integrations/text_embedding/ipex_llm.ipynb
- langchain/docs/docs/integrations/text_embedding/ipex_llm_gpu.ipynb
-
langchain/libs/community/tests/integration_tests/embeddings/test_ipex_llm.py
---------
Co-authored-by: Shengsheng Huang <shannie.huang@gmail.com>
Anthropic's streaming treats tool calls as different content parts
(streamed back with a different index) from normal content in the
`content`.
This means that we need to update our chunk-merging logic to handle
chunks with multi-part content. The alternative is coerceing Anthropic's
responses into a string, but we generally like to preserve model
provider responses faithfully when we can. This will also likely be
useful for multimodal outputs in the future.
This current PR does unfortunately make `index` a magic field within
content parts, but Anthropic and OpenAI both use it at the moment to
determine order anyway. To avoid cases where we have content arrays with
holes and to simplify the logic, I've also restricted merging to chunks
in order.
TODO: tests
CC @baskaryan @ccurme @efriis
- This fixes all the tracing issues with people still using
get_relevant_docs, and a change we need for 0.3 anyway
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- **Description:** The `ApifyWrapper` class expects `apify_api_token` to
be passed as a named parameter or set as an environment variable. But
the corresponding field was missing in the class definition causing the
argument to be ignored when passed as a named param. This patch fixes
that.
- This is a pattern that shows up occasionally in langgraph questions,
people chain a graph to something else after, and want to pass the graph
some kwargs (eg. stream_mode)
- [x] How to: use a vector store to retrieve data
- [ ] How to: generate multiple queries to retrieve data for
- [x] How to: use contextual compression to compress the data retrieved
- [x] How to: write a custom retriever class
- [x] How to: add similarity scores to retriever results
^ done last month
- [x] How to: combine the results from multiple retrievers
- [x] How to: reorder retrieved results to mitigate the "lost in the
middle" effect
- [x] How to: generate multiple embeddings per document
^ this PR
- [ ] How to: retrieve the whole document for a chunk
- [ ] How to: generate metadata filters
- [ ] How to: create a time-weighted retriever
- [ ] How to: use hybrid vector and keyword retrieval
^ todo
1/ added section at start with full code
2/ removed retriever tool (was just distracting)
3/ added section on starting a new conversation
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
LangSmith and LangChain context var handling evolved in parallel since
originally we didn't expect people to want to interweave the decorator
and langchain code.
Once we get a new langsmith release, this PR will let you seemlessly
hand off between @traceable context and runnable config context so you
can arbitrarily nest code.
It's expected that this fails right now until we get another release of
the SDK
### Issue: #22299
### descriptions
The documentation appears to be wrong. When the user actually sets this
parameter "asynchronous" to be True, it fails because the __init__
function of FAISS class doesn't allow this parameter. In fact, most of
the class/instance functions of this class have both the sync/async
version, so it looks like what we need is just to remove this parameter
from the doc.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Co-authored-by: Lifu Wu <lifu@nextbillion.ai>
- [x] Docs Update: Ollama
- llm/ollama
- Switched to using llama3 as model with reference to templating and
prompting
- Added concurrency notes to llm/ollama docs
- chat_models/ollama
- Added concurrency notes to llm/ollama docs
- text_embedding/ollama
- include example for specific embedding models from Ollama
- **Description:** This PR contains a bugfix which result in malfunction
of multi-turn conversation in QianfanChatEndpoint and adaption for
ToolCall and ToolMessage
ChatOpenAI supports a kwarg `stream_options` which can take values
`{"include_usage": True}` and `{"include_usage": False}`.
Setting include_usage to True adds a message chunk to the end of the
stream with usage_metadata populated. In this case the final chunk no
longer includes `"finish_reason"` in the `response_metadata`. This is
the current default and is not yet released. Because this could be
disruptive to workflows, here we remove this default. The default will
now be consistent with OpenAI's API (see parameter
[here](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stream_options)).
Examples:
```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
for chunk in llm.stream("hi"):
print(chunk)
```
```
content='' id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
content='Hello' id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
content='!' id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
content='' response_metadata={'finish_reason': 'stop'} id='run-8cff4721-2acd-4551-9bf7-1911dae46b92'
```
```python
for chunk in llm.stream("hi", stream_options={"include_usage": True}):
print(chunk)
```
```
content='' id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='Hello' id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='!' id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='' response_metadata={'finish_reason': 'stop'} id='run-39ab349b-f954-464d-af6e-72a0927daa27'
content='' id='run-39ab349b-f954-464d-af6e-72a0927daa27' usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17}
```
```python
llm = ChatOpenAI().bind(stream_options={"include_usage": True})
for chunk in llm.stream("hi"):
print(chunk)
```
```
content='' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='Hello' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='!' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='' response_metadata={'finish_reason': 'stop'} id='run-59918845-04b2-41a6-8d90-f75fb4506e0d'
content='' id='run-59918845-04b2-41a6-8d90-f75fb4506e0d' usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17}
```
Add kwargs in add_documents function
**langchain**: Add **kwargs in parent_document_retriever"
- **Add kwargs for `add_document` in `parent_document_retriever.py`**
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Issue: The `arXiv` page is missing the arxiv paper references from the
`langchain/cookbook`.
PR: Added the cookbook references.
Result: `Found 29 arXiv references in the 3 docs, 21 API Refs, 5
Templates, and 18 Cookbooks.` - much more references are visible now.
**Description:** Update langchainhub integration test dependency and add
an integration test for pulling private prompt
**Dependencies:** langchainhub 0.1.16
Change 'FIREWALL' to 'FIRECRAWL' as I believe this may have been in
error. Other docs refer to 'FIRECRAWL_API_KEY'.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description
## Problem
`Runnable.get_graph` fails when `InputType` or `OutputType` property
raises `TypeError`.
-
003c98e5b4/libs/core/langchain_core/runnables/base.py (L250-L274)
-
003c98e5b4/libs/core/langchain_core/runnables/base.py (L394-L396)
This problem prevents getting a graph of `Runnable` objects whose
`InputType` or `OutputType` property raises `TypeError` but whose
`invoke` works well, such as `langchain.output_parsers.RegexParser`,
which I have already pointed out in #19792 that a `TypeError` would
occur.
## Solution
- Add `try-except` syntax to handle `TypeError` to the codes which get
`input_node` and `output_node`.
# Issue
- #19801
# Twitter Handle
- [hmdev3](https://twitter.com/hmdev3)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [ ] **PR title**: "Fix list handling in Clova embeddings example
documentation"
- Description:
Fixes a bug in the Clova Embeddings example documentation where
document_text was incorrectly wrapped in an additional list.
- Rationale
The embed_documents method expects a list, but the previous example
wrapped document_text in an unnecessary additional list, causing an
error. The updated example correctly passes document_text directly to
the method, ensuring it functions as intended.
Added the missing verb "is" and a comma to the text in the Prompt
Templates description within the Build a Simple LLM Application tutorial
for more clarity.
- **Description:** updated documentation for llama, falcona and gemma on
Vertex AI Model garden
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
@lkuligin for review
---------
Co-authored-by: adityarane@google.com <adityarane@google.com>
Thank you for contributing to LangChain!
- [x] **PR title**: community: Add Zep Cloud components + docs +
examples
- [x] **PR message**:
We have recently released our new zep-cloud sdks that are compatible
with Zep Cloud (not Zep Open Source). We have also maintained our Cloud
version of langchain components (ChatMessageHistory, VectorStore) as
part of our sdks. This PRs goal is to port these components to langchain
community repo, and close the gap with the existing Zep Open Source
components already present in community repo (added
ZepCloudMemory,ZepCloudVectorStore,ZepCloudRetriever).
Also added a ZepCloudChatMessageHistory components together with an
expression language example ported from our repo. We have left the
original open source components intact on purpose as to not introduce
any breaking changes.
- **Issue:** -
- **Dependencies:** Added optional dependency of our new cloud sdk
`zep-cloud`
- **Twitter handle:** @paulpaliychuk51
- [x] **Add tests and docs**
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
3 fixes of DuckDB vector store:
- unify defaults in constructor and from_texts (users no longer have to
specify `vector_key`).
- include search similarity into output metadata (fixes#20969)
- significantly improve performance of `from_documents`
Dependencies: added Pandas to speed up `from_documents`.
I was thinking about CSV and JSON options, but I expect trouble loading
JSON values this way and also CSV and JSON options require storing data
to disk.
Anyway, the poetry file for langchain-community already contains a
dependency on Pandas.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Generates release notes based on a `git log` command with title names
Aiming to improve to splitting out features vs. bugfixes using
conventional commits in the coming weeks.
Will work for any monorepo packages
- **Description:** this PR gives clickhouse client the ability to use a
secure connection to the clickhosue server
- **Issue:** fixes#22082
- **Dependencies:** -
- **Twitter handle:** `_codingcoffee_`
Signed-off-by: Ameya Shenoy <shenoy.ameya@gmail.com>
Co-authored-by: Shresth Rana <shresth@grapevine.in>
OpenAI recently added a `stream_options` parameter to its chat
completions API (see [release
notes](https://platform.openai.com/docs/changelog/added-chat-completions-stream-usage)).
When this parameter is set to `{"usage": True}`, an extra "empty"
message is added to the end of a stream containing token usage. Here we
propagate token usage to `AIMessage.usage_metadata`.
We enable this feature by default. Streams would now include an extra
chunk at the end, **after** the chunk with
`response_metadata={'finish_reason': 'stop'}`.
New behavior:
```
[AIMessageChunk(content='', id='run-4b20dbe0-3817-4f62-b89d-03ef76f25bde'),
AIMessageChunk(content='Hello', id='run-4b20dbe0-3817-4f62-b89d-03ef76f25bde'),
AIMessageChunk(content='!', id='run-4b20dbe0-3817-4f62-b89d-03ef76f25bde'),
AIMessageChunk(content='', response_metadata={'finish_reason': 'stop'}, id='run-4b20dbe0-3817-4f62-b89d-03ef76f25bde'),
AIMessageChunk(content='', id='run-4b20dbe0-3817-4f62-b89d-03ef76f25bde', usage_metadata={'input_tokens': 8, 'output_tokens': 9, 'total_tokens': 17})]
```
Old behavior (accessible by passing `stream_options={"include_usage":
False}` into (a)stream:
```
[AIMessageChunk(content='', id='run-1312b971-c5ea-4d92-9015-e6604535f339'),
AIMessageChunk(content='Hello', id='run-1312b971-c5ea-4d92-9015-e6604535f339'),
AIMessageChunk(content='!', id='run-1312b971-c5ea-4d92-9015-e6604535f339'),
AIMessageChunk(content='', response_metadata={'finish_reason': 'stop'}, id='run-1312b971-c5ea-4d92-9015-e6604535f339')]
```
From what I can tell this is not yet implemented in Azure, so we enable
only for ChatOpenAI.
Thank you for contributing to LangChain!
- [X] **PR title**: community: Updated langchain-community PremAI
documentation
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Hey, I'm Sasha. The SDK engineer from [Comet](https://comet.com).
This PR updates the CometTracer class.
Added metadata to CometTracerr. From now on, both chains and spans will
send it.
* Lint for usage of standard xml library
* Add forced opt-in for quip client
* Actual security issue is with underlying QuipClient not LangChain
integration (since the client is doing the parsing), but adding
enforcement at the LangChain level.
- **Description:** I've added a tab on embedding text with LangChain
using Hugging Face models to here:
https://python.langchain.com/v0.2/docs/how_to/embed_text/. HF was
mentioned in the running text, but not in the tabs, which I thought was
odd.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** No need, this is tiny :)
Also, I had a ton of issues with the poetry docs/lint install, so I
haven't linted this. Apologies for that.
cc @Jofthomas
- Tom Aarsen
If tool_use blocks and tool_calls with overlapping IDs are present,
prefer the values of the tool_calls. Allows for mutating AIMessages just
via tool_calls.
**PR message**:
Update `hub.pull("rlm/map-prompt")` to `hub.pull("rlm/reduce-prompt")`
in summarization.ipynb
**Description:**
Fix typo in prompt hub link from `reduce_prompt =
hub.pull("rlm/map-prompt")` to `reduce_prompt =
hub.pull("rlm/reduce-prompt")` following next issue
**Issue:** #22014
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
```python
class UsageMetadata(TypedDict):
"""Usage metadata for a message, such as token counts.
Attributes:
input_tokens: (int) count of input (or prompt) tokens
output_tokens: (int) count of output (or completion) tokens
total_tokens: (int) total token count
"""
input_tokens: int
output_tokens: int
total_tokens: int
```
```python
class AIMessage(BaseMessage):
...
usage_metadata: Optional[UsageMetadata] = None
"""If provided, token usage information associated with the message."""
...
```
- **Description:** When I was running the sparkllm, I found that the
default parameters currently used could no longer run correctly.
- original parameters & values:
- spark_api_url: "wss://spark-api.xf-yun.com/v3.1/chat"
- spark_llm_domain: "generalv3"
```python
# example
from langchain_community.chat_models import ChatSparkLLM
spark = ChatSparkLLM(spark_app_id="my_app_id",
spark_api_key="my_api_key", spark_api_secret="my_api_secret")
spark.invoke("hello")
```
So I updated them to 3.5 (same as sparkllm official website). After the
update, they can be used normally.
- new parameters & values:
- spark_api_url: "wss://spark-api.xf-yun.com/v3.5/chat"
- spark_llm_domain: "generalv3.5"
This pull request addresses and fixes exception handling in the
UpstageLayoutAnalysisParser and enhances the test coverage by adding
error exception tests for the document loader. These improvements ensure
robust error handling and increase the reliability of the system when
dealing with external API calls and JSON responses.
### Changes Made
1. Fix Request Exception Handling:
- Issue: The existing implementation of UpstageLayoutAnalysisParser did
not properly handle exceptions thrown by the requests library, which
could lead to unhandled exceptions and potential crashes.
- Solution: Added comprehensive exception handling for
requests.RequestException to catch any request-related errors. This
includes logging the error details and raising a ValueError with a
meaningful error message.
2. Add Error Exception Tests for Document Loader:
- New Tests: Introduced new test cases to verify the robustness of the
UpstageLayoutAnalysisLoader against various error scenarios. The tests
ensure that the loader gracefully handles:
- RequestException: Simulates network issues or invalid API requests to
ensure appropriate error handling and user feedback.
- JSONDecodeError: Simulates scenarios where the API response is not a
valid JSON, ensuring the system does not crash and provides clear error
messaging.
**Description:**
- Added propagation of document metadata from O365BaseLoader to
FileSystemBlobLoader (O365BaseLoader uses FileSystemBlobLoader under the
hood).
- This is done by passing dictionary `metadata_dict`: key=filename and
value=dictionary containing document's metadata
- Modified `FileSystemBlobLoader` to accept the `metadata_dict`, use
`mimetype` from it (if available) and pass metadata further into blob
loader.
**Issue:**
- `O365BaseLoader` under the hood downloads documents to temp folder and
then uses `FileSystemBlobLoader` on it.
- However metadata about the document in question is lost in this
process. In particular:
- `mime_type`: `FileSystemBlobLoader` guesses `mime_type` from the file
extension, but that does not work 100% of the time.
- `web_url`: this is useful to keep around since in RAG LLM we might
want to provide link to the source document. In order to work well with
document parsers, we pass the `web_url` as `source` (`web_url` is
ignored by parsers, `source` is preserved)
**Dependencies:**
None
**Twitter handle:**
@martintriska1
Please review @baskaryan
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "Add CloudBlobLoader"
- community: Add CloudBlobLoader
- [ ] **PR message**: Add cloud blob loader
- **Description:**
Langchain provides several approaches to read different file formats:
Specific loaders (`CVSLoader`) or blob-compatible loaders
(`FileSystemBlobLoader`). The only implementation proposed for
BlobLoader is `FileSystemBlobLoader`.
Many projects retrieve files from cloud storage. We propose a new
implementation of `BlobLoader` to read files from the three cloud
storage systems. The interface is strictly identical to
`FileSystemBlobLoader`. The only difference is the constructor, which
takes a cloud "url" object such as `s3://my-bucket`, `az://my-bucket`,
or `gs://my-bucket`.
By streamlining the process, this novel implementation eliminates the
requirement to pre-download files from cloud storage to local temporary
files (which are seldom removed).
The code relies on the
[CloudPathLib](https://cloudpathlib.drivendata.org/stable/) library to
interpret cloud URLs. This has been added as an optional dependency.
```Python
loader = CloudBlobLoader("s3://mybucket/id")
for blob in loader.yield_blobs():
print(blob)
```
- [X] **Dependencies:** CloudPathLib
- [X] **Twitter handle:** pprados
- [X] **Add tests and docs**: Add unit test, but it's easy to convert to
integration test, with some files in a cloud storage (see
`test_cloud_blob_loader.py`)
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
Hello from Paris @hwchase17. Can you review this PR?
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
This PR contains 4 added functions:
- max_marginal_relevance_search_by_vector
- amax_marginal_relevance_search_by_vector
- max_marginal_relevance_search
- amax_marginal_relevance_search
I'm no langchain expert, but tried do inspect other vectorstore sources
like chroma, to build these functions for SurrealDB. If someone has some
changes for me, please let me know. Otherwise I would be happy, if these
changes are added to the repository, so that I can use the orignal repo
and not my local monkey patched version.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:https://github.com/arpitkumar980/langchain.git
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Fixed `AzureSearchVectorStoreRetriever` to account
for search_kwargs. More explanation is in the mentioned issue.
- **Issue:** #21492
---------
Co-authored-by: MAC <mac@MACs-MacBook-Pro.local>
Co-authored-by: Massimiliano Pronesti <massimiliano.pronesti@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [X] **PR title**: "docs: Chroma docstrings update"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [X] **PR message**:
- **Description:** Added and updated Chroma docstrings
- **Issue:** https://github.com/langchain-ai/langchain/issues/21983
- [X] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- only docs
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Description: This change adds args_schema (pydantic BaseModel) to
WikipediaQueryRun for correct schema formatting on LLM function calls
Issue: currently using WikipediaQueryRun with OpenAI function calling
returns the following error "TypeError: WikipediaQueryRun._run() got an
unexpected keyword argument '__arg1' ". This happens because the schema
sent to the LLM is "input: '{"__arg1":"Hunter x Hunter"}'" while the
method should be called with the "query" parameter.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added [Scrapfly](https://scrapfly.io/) Web Loader integration. Scrapfly
is a web scraping API that allows extracting web page data into
accessible markdown or text datasets.
- __Description__: Added Scrapfly web loader for retrieving web page
data as markdown or text.
- Dependencies: scrapfly-sdk
- Twitter: @thealchemi1st
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Updates Meilisearch vectorstore for compatibility
with v1.8. Adds [”showRankingScore”:
true”](https://www.meilisearch.com/docs/reference/api/search#ranking-score)
in the search parameters and replaces `_semanticScore` field with `
_rankingScore`
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:**
- Extend AzureSearch with `maximal_marginal_relevance` (for vector and
hybrid search)
- Add construction `from_embeddings` - if the user has already embedded
the texts
- Add `add_embeddings`
- Refactor common parts (`_simple_search`, `_results_to_documents`,
`_reorder_results_with_maximal_marginal_relevance`)
- Add `vector_search_dimensions` as a parameter to the constructor to
avoid extra calls to `embed_query` (most of the time the user applies
the same model and knows the dimension)
**Issue:** none
**Dependencies:** none
- [x] **Add tests and docs**: The docstrings have been added to the new
functions, and unified for the existing ones. The example notebook is
great in illustrating the main usage of AzureSearch, adding the new
methods would only dilute the main content.
- [x] **Lint and test**
---------
Co-authored-by: Oleksii Pokotylo <oleksii.pokotylo@pwc.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Backwards compatible extension of the initialisation
interface of HanaDB to allow the user to specify
specific_metadata_columns that are used for metadata storage of selected
keys which yields increased filter performance. Any not-mentioned
metadata remains in the general metadata column as part of a JSON
string. Furthermore switched to executemany for batch inserts into
HanaDB.
**Issue:** N/A
**Dependencies:** no new dependencies added
**Twitter handle:** @sapopensource
---------
Co-authored-by: Martin Kolb <martin.kolb@sap.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Added extra functionality to `CharacterTextSplitter`,
`TextSplitter` classes.
The user can select whether to append the separator to the previous
chunk with `keep_separator='end' ` or else prepend to the next chunk.
Previous functionality prepended by default to next chunk.
**Issue:** Fixes#20908
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Integrate RankLLM reranker (https://github.com/castorini/rank_llm) into
LangChain
An example notebook is given in
`docs/docs/integrations/retrievers/rankllm-reranker.ipynb`
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Bug code**: In
langchain_community/document_loaders/csv_loader.py:100
- **Description**: currently, when 'CSVLoader' reads the column as None
in the 'csv' file, it will report an error because the 'CSVLoader' does
not verify whether the column is of str type and does not consider how
to handle the corresponding 'row_data' when the column is' None 'in the
csv. This pr provides a solution.
- **Issue:** Fix#20699
- **thinking:**
1. Refer to the processing method for
'langchain_community/document_loaders/csv_loader.py:100' when **'v'**
equals'None', and apply the same method to '**k**'.
(Reference`csv.DictReader` ,**'k'** will only be None when `
len(columns) < len(number_row_data)` is established)
2. **‘k’** equals None only holds when it is the last column, and its
corresponding **'v'** type is a list. Therefore, I referred to the data
format in 'Document' and used ',' to concatenated the elements in the
list.(But I'm not sure if you accept this form, if you have any other
ideas, communicate)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Added revision_example prompt template to include the
revision request and revision examples in the revision chain.
**Issue:** Not Applicable
**Dependencies:** Not Applicable
**Twitter handle:** @nithinjp09
## Description
The existing public interface for `langchain_community.emeddings` is
broken. In this file, `__all__` is statically defined, but is
subsequently overwritten with a dynamic expression, which type checkers
like pyright do not support. pyright actually gives the following
diagnostic on the line I am requesting we remove:
[reportUnsupportedDunderAll](https://github.com/microsoft/pyright/blob/main/docs/configuration.md#reportUnsupportedDunderAll):
```
Operation on "__all__" is not supported, so exported symbol list may be incorrect
```
Currently, I get the following errors when attempting to use publicablly
exported classes in `langchain_community.emeddings`:
```python
import langchain_community.embeddings
langchain_community.embeddings.HuggingFaceEmbeddings(...) # error: "HuggingFaceEmbeddings" is not exported from module "langchain_community.embeddings" (reportPrivateImportUsage)
```
This is solved easily by removing the dynamic expression.
Thank you for contributing to LangChain!
- [X] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
**Description:**
Fix ChatDatabricsk in case that streaming response doesn't have role
field in delta chunk
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Updates docs so the example doesn't lead to a warning:
```
LangChainDeprecationWarning: Importing tools from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead:
`from langchain_community.tools import WikipediaQueryRun`.
To install langchain-community run `pip install -U langchain-community`.
```
## 'raise_for_status' parameter of WebBaseLoader works in sync load but
not in async load.
In webBaseLoader:
Sync load is calling `_scrape` and has `raise_for_status` properly
handled.
```
def _scrape(
self,
url: str,
parser: Union[str, None] = None,
bs_kwargs: Optional[dict] = None,
) -> Any:
from bs4 import BeautifulSoup
if parser is None:
if url.endswith(".xml"):
parser = "xml"
else:
parser = self.default_parser
self._check_parser(parser)
html_doc = self.session.get(url, **self.requests_kwargs)
if self.raise_for_status:
html_doc.raise_for_status()
if self.encoding is not None:
html_doc.encoding = self.encoding
elif self.autoset_encoding:
html_doc.encoding = html_doc.apparent_encoding
return BeautifulSoup(html_doc.text, parser, **(bs_kwargs or {}))
```
Async load is calling `_fetch` but missing `raise_for_status` logic.
```
async def _fetch(
self, url: str, retries: int = 3, cooldown: int = 2, backoff: float = 1.5
) -> str:
async with aiohttp.ClientSession() as session:
for i in range(retries):
try:
async with session.get(
url,
headers=self.session.headers,
ssl=None if self.session.verify else False,
cookies=self.session.cookies.get_dict(),
) as response:
return await response.text()
```
Co-authored-by: kefan.you <darkfss@sina.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "update IBM WatsonxLLM docs with deprecated
LLMChain"
- [x] **PR message**:
- **Description:** update IBM WatsonxLLM docs with deprecated LLMChain
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
**Title**: "langchain: OpenAI Assistants v2 api support"
***Descriptions***
- [x] "attachments" support added along with backward compatibility of
"file_ids"
- [x] "tool_resources" support added while creating new assistant
- [ ] "tool_choice" parameter support
- [ ] Streaming support
- **Dependencies:** OpenAI v2 API (openai>=1.23.0)
- **Twitter handle:** @skanta_rath
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Updated docs to have an example to use Jamba instead of J2
---------
Co-authored-by: Asaf Gardin <asafg@ai21.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Tongyi uses different client for chat model and
vision model. This PR chooses proper client based on model name to
support both chat model and vision model. Reference [tongyi
document](https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-plus-api?spm=a2c4g.11186623.0.0.27404c9a7upm11)
for details.
```
from langchain_core.messages import HumanMessage
from langchain_community.chat_models import ChatTongyi
llm = ChatTongyi(model_name='qwen-vl-max')
image_message = {
"image": "https://lilianweng.github.io/posts/2023-06-23-agent/agent-overview.png"
}
text_message = {
"text": "summarize this picture",
}
message = HumanMessage(content=[text_message, image_message])
llm.invoke([message])
```
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
- if tap_output_iter/aiter is called multiple times for the same run
issue events only once
- if chat model run is tapped don't issue duplicate on_llm_new_token
events
- if first chunk arrives after run has ended do not emit it as a stream
event
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- `llm_chain` becomes `Union[LLMChain, Runnable]`
- `.from_llm` creates a runnable
tested by verifying that docs/how_to/MultiQueryRetriever.ipynb runs
unchanged with sync/async invoke (and that it runs if we specifically
instantiate with LLMChain).
We add a tool and retriever for the [AskNews](https://asknews.app)
platform with example notebooks.
The retriever can be invoked with:
```py
from langchain_community.retrievers import AskNewsRetriever
retriever = AskNewsRetriever(k=3)
retriever.invoke("impact of fed policy on the tech sector")
```
To retrieve 3 documents in then news related to fed policy impacts on
the tech sector. The included notebook also includes deeper details
about controlling filters such as category and time, as well as
including the retriever in a chain.
The tool is quite interesting, as it allows the agent to decide how to
obtain the news by forming a query and deciding how far back in time to
look for the news:
```py
from langchain_community.tools.asknews import AskNewsSearch
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
tool = AskNewsSearch()
instructions = """You are an assistant."""
base_prompt = hub.pull("langchain-ai/openai-functions-template")
prompt = base_prompt.partial(instructions=instructions)
llm = ChatOpenAI(temperature=0)
asknews_tool = AskNewsSearch()
tools = [asknews_tool]
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
verbose=True,
)
agent_executor.invoke({"input": "How is the tech sector being affected by fed policy?"})
```
---------
Co-authored-by: Emre <e@emre.pm>
Please let me know if you see any possible areas of improvement. I would
very much appreciate your constructive criticism if time allows.
**Description:**
- Added a aerospike vector store integration that utilizes
[Aerospike-Vector-Search](https://aerospike.com/products/vector-database-search-llm/)
add-on.
- Added both unit tests and integration tests
- Added a docker compose file for spinning up a test environment
- Added a notebook
**Dependencies:** any dependencies required for this change
- aerospike-vector-search
**Twitter handle:**
- No twitter, you can use my GitHub handle or LinkedIn if you'd like
Thanks!
---------
Co-authored-by: Jesse Schumacher <jschumacher@aerospike.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Closes#20561
This PR fixes MLX LLM stream `AttributeError`.
Recently, `mlx-lm` changed the token decoding logic, which affected the
LC+MLX integration.
Additionally, I made minor fixes such as: docs example broken link and
enforcing pipeline arguments (max_tokens, temp and etc) for invoke.
- **Issue:** #20561
- **Twitter handle:** @Prince_Canuma
Related to #20085
@baskaryan
Thank you for contributing to LangChain!
community:sparkllm[patch]: standardized init args
updated `spark_api_key` so that aliased to `api_key`. Added integration
test for `sparkllm` to test that it continues to set the same underlying
attribute.
updated temperature with Pydantic Field, added to the integration test.
Ran `make format`,`make test`, `make lint`, `make spell_check`
UpTrain has a new dashboard now that makes it easier to view projects
and evaluations. Using this requires specifying both project_name and
evaluation_name when performing evaluations. I have updated the code to
support it.
Thank you for contributing to LangChain!
- [x] **PR title**: "community: enable SupabaseVectorStore to support
extended table fields"
- [x] **PR message**:
- Added extension fields to the function _add_vectors so that users can
add other custom fields when insert a record into the database. eg:
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**:
- Reference to `Collection` object is set to `None` when deleting a
collection `delete_collection()`
- Added utility method `reset_collection()` to allow recreating the
collection
- Moved collection creation out of `__init__` into
`__ensure_collection()` to be reused by object init and
`reset_collection()`
- `_collection` is now a property to avoid breaking changes
**Issues**:
- chroma-core/chroma#2213
**Twitter**: @t_azarov
Example error message:
line 206, in _get_python_function_required_args
if is_function_type and required[0] == "self":
~~~~~~~~^^^
IndexError: list index out of range
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
While integrating the xinference_embedding, we observed that the
downloaded dependency package is quite substantial in size. With a focus
on resource optimization and efficiency, if the project requirements are
limited to its vector processing capabilities, we recommend migrating to
the xinference_client package. This package is more streamlined,
significantly reducing the storage space requirements of the project and
maintaining a feature focus, making it particularly suitable for
scenarios that demand lightweight integration. Such an approach not only
boosts deployment efficiency but also enhances the application's
maintainability, rendering it an optimal choice for our current context.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Add `Origin/langchain` to Apify's client's user-agent
to attribute API activity to LangChain (at Apify, we aim to monitor our
integrations to evaluate whether we should invest more in the LangChain
integration regarding functionality and content)
**Issue:** None
**Dependencies:** None
**Twitter handle:** None
## Description
This PR implements local and dynamic mode in the Nomic Embed integration
using the inference_mode and device parameters. They work as documented
[here](https://docs.nomic.ai/reference/python-api/embeddings#local-inference).
<!-- If no one reviews your PR within a few days, please @-mention one
of baskaryan, efriis, eyurtsev, hwchase17. -->
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
These packages all import `LangSmithParams` which was released in
langchain-core==0.2.0.
N.B. we will need to release `openai` and then bump `langchain-openai`
in `together` and `upstage`.
Thank you for contributing to LangChain!
- [x] **PR title**: "docs: update notebook for latest Pinecone API +
serverless"
- [x] **PR message**: Published notebook is incompatible with latest
`pinecone-client` and not runnable. Updated for use with latest Pinecone
Python SDK. Also updated to be compatible with serverless indexes (only
index type available on Pinecone free tier).
- [x] **Add tests and docs**: N/A (tested in Colab)
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---
- To see the specific tasks where the Asana app for GitHub is being
used, see below:
- https://app.asana.com/0/0/1207328087952499
Thank you for contributing to LangChain!
- [x] **PR title**: "docs: update notebook for new Pinecone API +
serverless"
- [x] **PR message**: The published notebook is not runnable after
`pinecone-client` v2, which is deprecated. `langchain-pinecone` is not
compatible with the latest `pinecone-client` (v4), so I hardcoded it to
the last v3. Also updated for serverless indexes (only index type
available on Pinecone free plan).
- [x] **Add tests and docs**: N/A (tested in Colab)
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---
- To see the specific tasks where the Asana app for GitHub is being
used, see below:
- https://app.asana.com/0/0/1207328087952500
This PR fixes two mistakes in the import paths from community for the
json data aiding the cli migration to 0.2.
It is intended as a quick follow-up to
https://github.com/langchain-ai/langchain/pull/21913 .
@nicoloboschi FYI
ChatOpenaAI --> ChatOpenAI
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Thank you for contributing to LangChain!
Remove unnecessary print from voyageai embeddings
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
- bind_tools interface is a better alternative.
- openai doesn't use functions but tools in its API now.
- the underlying content appears in some redirects, so will need to
investigate if we can remove.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Check if event stream is closed in memory loop.
Using try/except here to avoid race condition, but this may incur a
small overhead in versions prios to 3.11
Update tool calling using prompts.
- Add required concepts
- Update names of tool invoking function.
- Add doc-string to function, and add information about `config` (which
users often forget)
- Remove steps that show how to use single function only. This makes the
how-to guide a bit shorter and more to the point.
- Add diagram from another how-to guide that shows how the thing works
overall.
Since the LangChain based on many research papers, the LC documentation
has several references to the arXiv papers. It would be beneficial to
create a single page with all referenced papers.
PR:
1. Developed code to search the arXiv references in the LangChain
Documentation and the LangChain code base. Those references are included
in a newly generated documentation page.
2. Page is linked to the Docs menu.
Controversial:
1. The `arxiv_references` page is automatically generated. But this
generation now started only manually. It is not included in the doc
generation scripts. The reason for this is simple. I don't want to
mangle into the current documentation refactoring. If you think, we need
to regenerate this page in each build, let me know. Note: This script
has a dependency on the `arxiv` package.
2. The link for this page in the menu is not obvious.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Code:** langchain_community/embeddings/baichuan.py:82
- **Description:** When I make an error using 'baichuan embeddings', the
printed error message is wrapped (there is actually no need to wrap)
```python
# example
from langchain_community.embeddings import BaichuanTextEmbeddings
# error key
BAICHUAN_API_KEY = "sk-xxxxxxxxxxxxx"
embeddings = BaichuanTextEmbeddings(baichuan_api_key=BAICHUAN_API_KEY)
text_1 = "今天天气不错"
query_result = embeddings.embed_query(text_1)
```
There are 2 issues fixed here:
* In the notebook pandas dataframes are formatted as HTML in the cells.
On the documentation site the renderer that converts notebooks
incorrectly displays the raw HTML. I can't find any examples of where
this is working and so I am formatting the dataframes as text.
* Some incorrect table names were referenced resulting in errors.
The only change is replacing the word "operators" with "operates," to
make the sentence grammatically correct.
Thank you for contributing to LangChain!
- [x] **PR title**: "docs: Made a grammatical correction in
streaming.ipynb to use the word "operates" instead of the word
"operators""
- [x] **PR message**:
- **Description:** The use of the word "operators" was incorrect, given
the context and grammar of the sentence. This PR updates the
documentation to use the word "operates" instead of the word
"operators".
- **Issue:** Makes the documentation more easily understandable.
- **Dependencies:** -no dependencies-
- **Twitter handle:** --
- [x] **Add tests and docs**: Since no new integration is being made, no
new tests/example notebooks are required.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
- **No formatting changes made to the documentation**
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
- Remove double implementations of functions. The single input is just
taking up space.
- Added tool specific information for `async + showing invoke vs.
ainvoke.
- Added more general information about about `async` (this should live
in a different place eventually since it's not specific to tools).
- Changed ordering of custom tools (StructuredTool is simpler and should
appear before the inheritance)
- Improved the error handling section (not convinced it should be here
though)
- Add information about naitve tool calling capabilities
- Add information about standard langchain interface for tool calling
- Update description for tools
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
This PR improves on the `CassandraCache` and `CassandraSemanticCache`
classes, mainly in the constructor signature, and also introduces
several minor improvements around these classes.
### Init signature
A (sigh) breaking change is tentatively introduced to the constructor.
To me, the advantages outweigh the possible discomfort: the new syntax
places the DB-connection objects `session` and `keyspace` later in the
param list, so that they can be given a default value. This is what
enables the pattern of _not_ specifying them, provided one has
previously initialized the Cassandra connection through the versatile
utility method `cassio.init(...)`.
In this way, a much less unwieldy instantiation can be done, such as
`CassandraCache()` and `CassandraSemanticCache(embedding=xyz)`,
everything else falling back to defaults.
A downside is that, compared to the earlier signature, this might turn
out to be breaking for those doing positional instantiation. As a way to
mitigate this problem, this PR typechecks its first argument trying to
detect the legacy usage.
(And to make this point less tricky in the future, most arguments are
left to be keyword-only).
If this is considered too harsh, I'd like guidance on how to further
smoothen this transition. **Our plan is to make the pattern of optional
session/keyspace a standard across all Cassandra classes**, so that a
repeatable strategy would be ideal. A possibility would be to keep
positional arguments for legacy reasons but issue a deprecation warning
if any of them is actually used, to later remove them with 0.2 - please
advise on this point.
### Other changes
- class docstrings: enriched, completely moved to class level, added
note on `cassio.init(...)` pattern, added tiny sample usage code.
- semantic cache: revised terminology to never mention "distance" (it is
in fact a similarity!). Kept the legacy constructor param with a
deprecation warning if used.
- `llm_caching` notebook: uniform flow with the Cassandra and Astra DB
separate cases; better and Cassandra-first description; all imports made
explicit and from community where appropriate.
- cache integration tests moved to community (incl. the imported tools),
env var bugfix for `CASSANDRA_CONTACT_POINTS`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
## Patch Summary
community:openai[patch]: standardize init args
## Details
I made changes to the OpenAI Chat API wrapper test in the Langchain
open-source repository
- **File**: `libs/community/tests/unit_tests/chat_models/test_openai.py`
- **Changes**:
- Updated `max_retries` with Pydantic Field
- Updated the corresponding unit test
- **Related Issues**: #20085
- Updated max_retries with Pydantic Field, updated the unit test.
---------
Co-authored-by: JuHyung Son <sonju0427@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: updated Browserbase loader"
- [x] **PR message**:
Updates the Browserbase loader with more options and improved docs.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Do not prefix function signature
---
* Reason for this is that information is already present with tool
calling models.
* This will save on tokens for those models, and makes it more obvious
what the description is!
* The @tool can get more parameters to allow a user to re-introduce the
the signature if we want
To permit proper coercion of objects like the following:
```python
class MyAsyncCallable:
async def __call__(self, foo):
return await ...
class MyAsyncGenerator:
async def __call__(self, foo):
await ...
yield
```
This PR introduces a v2 implementation of astream events that removes
intermediate abstractions and fixes some issues with v1 implementation.
The v2 implementation significantly reduces relevant code that's
associated with the astream events implementation together with
overhead.
After this PR, the astream events implementation:
- Uses an async callback handler
- No longer relies on BaseTracer
- No longer relies on json patch
As a result of this re-write, a number of issues were discovered with
the existing implementation.
## Changes in V2 vs. V1
### on_chat_model_end `output`
The outputs associated with `on_chat_model_end` changed depending on
whether it was within a chain or not.
As a root level runnable the output was:
```python
"data": {"output": AIMessageChunk(content="hello world!", id='some id')}
```
As part of a chain the output was:
```
"data": {
"output": {
"generations": [
[
{
"generation_info": None,
"message": AIMessageChunk(
content="hello world!", id=AnyStr()
),
"text": "hello world!",
"type": "ChatGenerationChunk",
}
]
],
"llm_output": None,
}
},
```
After this PR, we will always use the simpler representation:
```python
"data": {"output": AIMessageChunk(content="hello world!", id='some id')}
```
**NOTE** Non chat models (i.e., regular LLMs) are still associated with
the more verbose format.
### Remove some `_stream` events
`on_retriever_stream` and `on_tool_stream` events were removed -- these
were not real events, but created as an artifact of implementing on top
of astream_log.
The same information is already available in the `x_on_end` events.
### Propagating Names
Names of runnables have been updated to be more consistent
```python
model = GenericFakeChatModel(messages=infinite_cycle).configurable_fields(
messages=ConfigurableField(
id="messages",
name="Messages",
description="Messages return by the LLM",
)
)
```
Before:
```python
"name": "RunnableConfigurableFields",
```
After:
```python
"name": "GenericFakeChatModel",
```
### on_retriever_end
on_retriever_end will always return `output` which is a list of
documents (rather than a dict containing a key called "documents")
### Retry events
Removed the `on_retry` callback handler. It was incorrectly showing that
the failed function being retried has invoked `on_chain_end`
https://github.com/langchain-ai/langchain/pull/21638/files#diff-e512e3f84daf23029ebcceb11460f1c82056314653673e450a5831147d8cb84dL1394
Add unit tests that show differences between sync / async versions when
streaming.
The inner on_chain_chunk event is missing if mixing sync and async
functionality. Likely due to missing tap_output_iter implementation on
the sync variant of `_transform_stream_with_config`
0.2 is not a breaking release for core (but it is for langchain and
community)
To keep the core+langchain+community packages in sync at 0.2, we will
relax deps throughout the ecosystem to tolerate `langchain-core` 0.2
## Description
This PR introduces the new `langchain-qdrant` partner package, intending
to deprecate the community package.
## Changes
- Moved the Qdrant vector store implementation `/libs/partners/qdrant`
with integration tests.
- The conditional imports of the client library are now regular with
minor implementation improvements.
- Added a deprecation warning to
`langchain_community.vectorstores.qdrant.Qdrant`.
- Replaced references/imports from `langchain_community` with either
`langchain_core` or by moving the definitions to the `langchain_qdrant`
package itself.
- Updated the Qdrant vector store documentation to reflect the changes.
## Testing
- `QDRANT_URL` and
[`QDRANT_API_KEY`](583e36bf6b)
env values need to be set to [run integration
tests](d608c93d1f)
in the [cloud](https://cloud.qdrant.tech).
- If a Qdrant instance is running at `http://localhost:6333`, the
integration tests will use it too.
- By default, tests use an
[`in-memory`](https://github.com/qdrant/qdrant-client?tab=readme-ov-file#local-mode)
instance(Not comprehensive).
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Erick Friis <erickfriis@gmail.com>
This PR makes some small updates for `KuzuQAChain` for graph QA.
- Updated Cypher generation prompt (we now support `WHERE EXISTS`) and
generalize it more
- Support different LLMs for Cypher generation and QA
- Update docs and examples
- Adds Techniques section
- Moves function calling, retrieval types to Techniques
- Removes Installation section (not conceptual)
- Reorders a few things (chat models before llms, package descriptions
before diagram)
- Add text splitter types to Techniques
First Pr for the langchain_huggingface partner Package
- Moved some of the hugging face related class from `community` to the
new `partner package`
Still needed :
- Documentation
- Tests
- Support for the new apply_chat_template in `ChatHuggingFace`
- Confirm choice of class to support for embeddings witht he
sentence-transformer team.
cc : @efriis
---------
Co-authored-by: Cyril Kondratenko <kkn1993@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- Introduce the `merge_and_split` function in the
`UpstageLayoutAnalysisLoader`.
- The `merge_and_split` function takes a list of documents and a
splitter as inputs.
- This function merges all documents and then divides them using the
`split_documents` method, which is a proprietary function of the
splitter.
- If the provided splitter is `None` (which is the default setting), the
function will simply merge the documents without splitting them.
- Make sure the left nav bar is horizontally scrollable
- Make sure the navigation dropdown is vertically scrollable and height
capped at 80% of viewport height
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Adds a Python REPL that executes code in a code interpreter session
using Azure Container Apps dynamic sessions.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [X] **PR title**: "community: Add source metadata to bedrock retriever
response"
- [X] **PR message**:
- **Description:** Bedrock retrieve API returns extra metadata in the
response which is currently not returned in the retriever response
- **Issue:** The change adds the metadata from bedrock retrieve API
response to the bedrock retriever in a backward compatible way. Renamed
metadata to sourceMetadata as metadata term is being used in the
Document already. This is in sync with what we are doing in llama-index
as well.
- **Dependencies:** No
- [X] **Add tests and docs**:
1. Added unit tests
2. Notebook already exists and does not need any change
3. Response from end to end testing, just to ensure backward
compatibility: `[Document(page_content='Exoplanets.',
metadata={'location': {'s3Location': {'uri':
's3://bucket/file_name.txt'}, 'type': 'S3'}, 'score': 0.46886647,
'source_metadata': {'x-amz-bedrock-kb-source-uri':
's3://bucket/file_name.txt', 'tag': 'space', 'team': 'Nasa', 'year':
1946.0}})]`
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
**Description:** Added a few additional arguments to the whisper parser,
which can be consumed by the underlying API.
The prompt is especially important to fine-tune transcriptions.
---------
Co-authored-by: Roi Perlman <roi@fivesigmalabs.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
This PR introduces chunking logic to the `DeepInfraEmbeddings` class to
handle large batch sizes without exceeding maximum batch size of the
backend. This enhancement ensures that embedding generation processes
large batches by breaking them down into smaller, manageable chunks,
each conforming to the maximum batch size limit.
**Issue:**
Fixes#21189
**Dependencies:**
No new dependencies introduced.
- Added new document_transformer: MarkdonifyTransformer, that uses
`markdonify` package with customizable options to convert HTML to
Markdown. It's similar to Html2TextTransformer, but has more flexible
options and also I've noticed that sometimes MarkdownifyTransformer
performs better than html2text one, so that's why I use markdownify on
my project.
- Added docs and tests
- Usage:
```python
from langchain_community.document_transformers import MarkdownifyTransformer
markdownify = MarkdownifyTransformer()
docs_transform = markdownify.transform_documents(docs)
```
- Example of better performance on simple task, that I've noticed:
```
<html>
<head><title>Reports on product movement</title></head>
<body>
<p data-block-key="2wst7">The reports on product movement will be useful for forming supplier orders and controlling outcomes.</p>
</body>
```
**Html2TextTransformer**:
```python
[Document(page_content='The reports on product movement will be useful for forming supplier orders and\ncontrolling outcomes.\n\n')]
# Here we can see 'and\ncontrolling', which has extra '\n' in it
```
**MarkdownifyTranformer**:
```python
[Document(page_content='Reports on product movement\n\nThe reports on product movement will be useful for forming supplier orders and controlling outcomes.')]
```
---------
Co-authored-by: Sokolov Fedor <f.sokolov@sokolov-macbook.bbrouter>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Sokolov Fedor <f.sokolov@sokolov-macbook.local>
Co-authored-by: Sokolov Fedor <f.sokolov@192.168.1.6>
### GPT4AllEmbeddings parameters
---
**Description:**
As of right now the **Embed4All** class inside _GPT4AllEmbeddings_ is
instantiated as it's default which leaves no room to customize the
chosen model and it's behavior. Thus:
- GPT4AllEmbeddings can now be instantiated with custom parameters like
a different model that shall be used.
---------
Co-authored-by: AlexJauchWalser <alexander.jauch-walser@knime.com>
The `_amake_session()` method does not allow modifying the
`self.session_factory` with
anything other than `async_sessionmaker`. This prohibits advanced uses
of `index()`.
In a RAG architecture, it is necessary to import document chunks.
To keep track of the links between chunks and documents, we can use the
`index()` API.
This API proposes to use an SQL-type record manager.
In a classic use case, using `SQLRecordManager` and a vector database,
it is impossible
to guarantee the consistency of the import. Indeed, if a crash occurs
during the import
(problem with the network, ...)
there is an inconsistency between the SQL database and the vector
database.
With the
[PR](https://github.com/langchain-ai/langchain-postgres/pull/32) we are
proposing for `langchain-postgres`,
it is now possible to guarantee the consistency of the import of chunks
into
a vector database. It's possible only if the outer session is built
with the connection.
```python
def main():
db_url = "postgresql+psycopg://postgres:password_postgres@localhost:5432/"
engine = create_engine(db_url, echo=True)
embeddings = FakeEmbeddings()
pgvector:VectorStore = PGVector(
embeddings=embeddings,
connection=engine,
)
record_manager = SQLRecordManager(
namespace="namespace",
engine=engine,
)
record_manager.create_schema()
with engine.connect() as connection:
session_maker = scoped_session(sessionmaker(bind=connection))
# NOTE: Update session_factories
record_manager.session_factory = session_maker
pgvector.session_maker = session_maker
with connection.begin():
loader = CSVLoader(
"data/faq/faq.csv",
source_column="source",
autodetect_encoding=True,
)
result = index(
source_id_key="source",
docs_source=loader.load()[:1],
cleanup="incremental",
vector_store=pgvector,
record_manager=record_manager,
)
print(result)
```
The same thing is possible asynchronously, but a bug in
`sql_record_manager.py`
in `_amake_session()` must first be fixed.
```python
async def _amake_session(self) -> AsyncGenerator[AsyncSession, None]:
"""Create a session and close it after use."""
# FIXME: REMOVE if not isinstance(self.session_factory, async_sessionmaker):~~
if not isinstance(self.engine, AsyncEngine):
raise AssertionError("This method is not supported for sync engines.")
async with self.session_factory() as session:
yield session
```
Then, it is possible to do the same thing asynchronously:
```python
async def main():
db_url = "postgresql+psycopg://postgres:password_postgres@localhost:5432/"
engine = create_async_engine(db_url, echo=True)
embeddings = FakeEmbeddings()
pgvector:VectorStore = PGVector(
embeddings=embeddings,
connection=engine,
)
record_manager = SQLRecordManager(
namespace="namespace",
engine=engine,
async_mode=True,
)
await record_manager.acreate_schema()
async with engine.connect() as connection:
session_maker = async_scoped_session(
async_sessionmaker(bind=connection),
scopefunc=current_task)
record_manager.session_factory = session_maker
pgvector.session_maker = session_maker
async with connection.begin():
loader = CSVLoader(
"data/faq/faq.csv",
source_column="source",
autodetect_encoding=True,
)
result = await aindex(
source_id_key="source",
docs_source=loader.load()[:1],
cleanup="incremental",
vector_store=pgvector,
record_manager=record_manager,
)
print(result)
asyncio.run(main())
```
---------
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Sean <sean@upstage.ai>
Co-authored-by: JuHyung-Son <sonju0427@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: YISH <mokeyish@hotmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Jason_Chen <820542443@qq.com>
Co-authored-by: Joan Fontanals <joan.fontanals.martinez@jina.ai>
Co-authored-by: Pavlo Paliychuk <pavlo.paliychuk.ca@gmail.com>
Co-authored-by: fzowl <160063452+fzowl@users.noreply.github.com>
Co-authored-by: samanhappy <samanhappy@gmail.com>
Co-authored-by: Lei Zhang <zhanglei@apache.org>
Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com>
Co-authored-by: merdan <48309329+merdan-9@users.noreply.github.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Andres Algaba <andresalgaba@gmail.com>
Co-authored-by: davidefantiniIntel <115252273+davidefantiniIntel@users.noreply.github.com>
Co-authored-by: Jingpan Xiong <71321890+klaus-xiong@users.noreply.github.com>
Co-authored-by: kaka <kaka@zbyte-inc.cloud>
Co-authored-by: jingsi <jingsi@leadincloud.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Rahul Triptahi <rahul.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Shengsheng Huang <shannie.huang@gmail.com>
Co-authored-by: Michael Schock <mjschock@users.noreply.github.com>
Co-authored-by: Anish Chakraborty <anish749@users.noreply.github.com>
Co-authored-by: am-kinetica <85610855+am-kinetica@users.noreply.github.com>
Co-authored-by: Dristy Srivastava <58721149+dristysrivastava@users.noreply.github.com>
Co-authored-by: Matt <matthew.gotteiner@microsoft.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
- **Description:** Fix import class name exporeted from
'playwright.async_api' and 'playwright.sync_api' to match the correct
name in playwright tool. Change import from inline guard_import to
helper function that calls guard_import to make code more readable in
gmail tool. Upgrade playwright version to 1.43.0
- **Issue:** #21354
- **Dependencies:** upgrade playwright version(this is not required for
the bugfix itself, just trying to keep dependencies fresh. I can remove
the playwright version upgrade if you want.)
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
0.2rc
migrations
- [x] Move memory
- [x] Move remaining retrievers
- [x] graph_qa chains
- [x] some dependency from evaluation code potentially on math utils
- [x] Move openapi chain from `langchain.chains.api.openapi` to
`langchain_community.chains.openapi`
- [x] Migrate `langchain.chains.ernie_functions` to
`langchain_community.chains.ernie_functions`
- [x] migrate `langchain/chains/llm_requests.py` to
`langchain_community.chains.llm_requests`
- [x] Moving `langchain_community.cross_enoders.base:BaseCrossEncoder`
->
`langchain_community.retrievers.document_compressors.cross_encoder:BaseCrossEncoder`
(namespace not ideal, but it needs to be moved to `langchain` to avoid
circular deps)
- [x] unit tests langchain -- add pytest.mark.community to some unit
tests that will stay in langchain
- [x] unit tests community -- move unit tests that depend on community
to community
- [x] mv integration tests that depend on community to community
- [x] mypy checks
Other todo
- [x] Make deprecation warnings not noisy (need to use warn deprecated
and check that things are implemented properly)
- [x] Update deprecation messages with timeline for code removal (likely
we actually won't be removing things until 0.4 release) -- will give
people more time to transition their code.
- [ ] Add information to deprecation warning to show users how to
migrate their code base using langchain-cli
- [ ] Remove any unnecessary requirements in langchain (e.g., is
SQLALchemy required?)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Robocorp (action server) toolkit had a limitation that the content
length returned by the tool was always cut to max 5000 chars. This was
from the time when context windows were much more limited.
This PR removes the limitation. Whatever the underlying tool provides
gets sent back to the agent.
As the robocorp toolkit no longer restricts the content, the implication
is that either the Action (tool) developer or the agent developer needs
to be aware of potentially oversized tool responses. Our point of view
is this should be the agent developer's responsibility, them being in
control of the use case and aware of the context window the LLM has.
Description: We are merging UPSTAGE_DOCUMENT_AI_API_KEY and
UPSTAGE_API_KEY into one, and only UPSTAGE_API_KEY will be used going
forward. And we changed the base class of ChatUpstage to BaseChatOpenAI.
---------
Co-authored-by: Sean <chosh0615@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **PR title**: "langchain-ibm: Fix llm and embeddings 'verify'
attribute default value"
- [x] **PR message**:
- **Description:** fix default value of "verify" attribute
- **Dependencies:** `ibm_watsonx_ai`
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Co-authored-by: Erick Friis <erick@langchain.dev>
…Endpoint`
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** add `bind_tools` and `with_structured_output` support
to `QianfanChatEndpoint`
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
- Added Together docs in chat models section
- Update Together provider docs to match the LLM & chat models sections
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This is a doc update. It fixes up formatting and product name
references. The example code is updated to use a local built-in text
file.
@mmhangami Please take a look
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** Updated the together integration docs by leading with
the streaming example, explicitly specifying a model to show users how
to do that, and updating the sections to more closely match other
integrations.
Description: This PR includes fix for loader_source to be fetched from
metadata in case of GdriveLoaders.
Documentation: NA
Unit Test: NA
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
- it's only node ids that are limited
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Thank you for contributing to LangChain!
- [ ] **HuggingFaceInferenceAPIEmbeddings**: "Additional Headers"
- Where: langchain, community, embeddings. huggingface.py.
- Community: add additional headers when needed by custom HuggingFace
TEI embedding endpoints. HuggingFaceInferenceAPIEmbeddings"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Adding the `additional_headers` to be passed to
requests library if needed
- **Dependencies:** none
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. Tested with locally available TEI endpoints with and without
`additional_headers`
2. Example Usage
```python
embeddings=HuggingFaceInferenceAPIEmbeddings(
api_key=MY_CUSTOM_API_KEY,
api_url=MY_CUSTOM_TEI_URL,
additional_headers={
"Content-Type": "application/json"
}
)
```
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Massimiliano Pronesti <massimiliano.pronesti@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** Adding chat completions to the Together AI package,
which is our most popular API. Also staying backwards compatible with
the old API so folks can continue to use the completions API as well.
Also moved the embedding API to use the OpenAI library to standardize it
further.
**Twitter handle:** @nutlope
- [x] **Add tests and docs**: If you're adding a new integration, please
include
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
[Standardized model init args
#20085](https://github.com/langchain-ai/langchain/issues/20085)
- Enable premai chat model to be initialized with `model_name` as an
alias for `model`, `api_key` as an alias for `premai_api_key`.
- Add initialization test `test_premai_initialization`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
- **Description:** fix: variable names in root validator not allowing
pass credentials as named parameters in llm instancing, also added
sambanova's sambaverse and sambastudio llms to __init__.py for module
import
Description: this change adds args_schema (pydantic BaseModel) to
YahooFinanceNewsTool for correct schema formatting on LLM function calls
Issue: currently using YahooFinanceNewsTool with OpenAI function calling
returns the following error "TypeError("YahooFinanceNewsTool._run() got
an unexpected keyword argument '__arg1'")". This happens because the
schema sent to the LLM is "input: "{'__arg1': 'MSFT'}"" while the method
should be called with the "query" parameter.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Issue: `load_qa_chain` is placed in the __init__.py file. As a result,
it is not listed in the API Reference docs.
BTW `load_qa_chain` is heavily presented in the doc examples, but is
missed in API Ref.
Change: moved code from init.py into a new file. Related: #21266
Reverts langchain-ai/langchain#21174
Hey team - going to revert this because it doesn't seem necessary for
testing. We should only be adding optional + extended_testing
dependencies for deps that have extended tests.
otherwise it just increases probability of dependency conflicts in the
community lockfile.
Thank you for contributing to LangChain!
community:baichuan[patch]: standardize init args
updated `baichuan_api_key` so that aliased to `api_key`. Added test that
it continues to set the same underlying attribute. Test checks for
`SecretStr`
updated `temperature` with Pydantic Field, added unit test.
Related to https://github.com/langchain-ai/langchain/issues/20085
If Session and/or keyspace are not provided, they are resolved from
cassio's context. So they are not required.
This change is fully backward compatible.
Issue: the `langkit` package is not presented in the `pyproject.toml`
but it is a requirement for the `WhyLabsCallbackHandler`
Change: added `langkit`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Update LarkSuite loader doc to give an example for
loading data from LarkSuite wiki.
**Issue:** None
**Dependencies:** None
**Twitter handle:** None
Thank you for contributing to LangChain!
- [x] **PR title**: "langchain-ibm: Add support for ibm-watsonx-ai new
major version"
- [x] **PR message**:
- **Description:** Add support for ibm-watsonx-ai new major version
- **Dependencies:** `ibm_watsonx_ai`
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
The `LocalFileStore` class can be used to create an on-disk
`CacheBackedEmbeddings` cache. The number of files in these embeddings
caches can grow to be quite large over time (hundreds of thousands) as
embeddings are computed for new versions of content, but the embeddings
for old/deprecated content are not removed.
A *least-recently-used* (LRU) cache policy could be applied to the
`LocalFileStore` directory to delete cache entries that have not been
referenced for some time:
```bash
# delete files that have not been accessed in the last 90 days
find embeddings_cache_dir/ -atime 90 -print0 | xargs -0 rm
```
However, most filesystems in enterprise environments disable access time
modification on read to improve performance. As a result, the access
times of these cache entry files are not updated when their values are
read.
To resolve this, this pull request updates the `LocalFileStore`
constructor to offer an `update_atime` parameter that causes access
times to be updated when a cache entry is read.
For example,
```python
file_store = LocalFileStore(temp_dir, update_atime=True)
```
The default is `False`, which retains the original behavior.
**Testing:**
I updated the LocalFileStore unit tests to test the access time update.
Before you could only extract triples (diffbot calls it facts) from
diffbot to avoid isolated nodes. However, sometimes isolated nodes can
still be useful like for prefiltering, so we want to allow users to
extract them if they want. Default behaviour is unchanged.
**Description:** Update unit test for ChatAnthropic
**Issue:** Test for key passed in from the environment should not have
the key initialized in the constructor
**Dependencies:** None
Thank you for contributing to LangChain!
- Oracle AI Vector Search
Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
- Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
This Pull Requests Adds the following functionalities
Oracle AI Vector Search : Vector Store
Oracle AI Vector Search : Document Loader
Oracle AI Vector Search : Document Splitter
Oracle AI Vector Search : Summary
Oracle AI Vector Search : Oracle Embeddings
- We have added unit tests and have our own local unit test suite which
verifies all the code is correct. We have made sure to add guides for
each of the components and one end to end guide that shows how the
entire thing runs.
- We have made sure that make format and make lint run clean.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com>
Co-authored-by: hroyofc <harichandan.roy@oracle.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Memory return could be set as `str` or `message` by `return_messages`
flag as mentioned in
https://python.langchain.com/docs/modules/memory/#whether-memory-is-a-string-or-a-list-of-messages,
where
`langchain.chains.conversation.memory.ConversationSummaryBufferMemory`
did not implement that.
This commit added `buffer_as_str` and `buffer_as_messages` function, and
`buffer` now affected by `return_messages` flag.
## Example Test Code and Output
```python
# Fix: ConversationSummaryBufferMemory with return_messages flag function
# Test code
from langchain.chains.conversation.memory import ConversationSummaryBufferMemory
from langchain_community.llms.ollama import Ollama
llm = Ollama()
# Create an instance of ConversationSummaryBufferMemory with return_messages set to True
memory = ConversationSummaryBufferMemory(return_messages=True, llm=llm)
# Add user and AI messages to the chat memory
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("what's up?")
# Print the buffer
print("Buffer:")
print(*map(type, memory.buffer), sep="\n")
print(memory.buffer, "\n")
# Print the buffer as a string
print("Buffer as String:")
print(type(memory.buffer_as_str))
print(memory.buffer_as_str, "\n")
# Print the buffer as messages
print("Buffer as Messages:")
print(*map(type, memory.buffer_as_messages), sep="\n")
print(memory.buffer_as_messages, "\n")
# Print the buffer after setting return_messages to False
memory.return_messages = False
print("Buffer after setting return_messages to False:")
print(type(memory.buffer))
print(memory.buffer, "\n")
```
```plaintext
Buffer:
<class 'langchain_core.messages.human.HumanMessage'>
<class 'langchain_core.messages.ai.AIMessage'>
[HumanMessage(content='hi!'), AIMessage(content="what's up?")]
Buffer as String:
<class 'str'>
Human: hi!
AI: what's up?
Buffer as Messages:
<class 'langchain_core.messages.human.HumanMessage'>
<class 'langchain_core.messages.ai.AIMessage'>
[HumanMessage(content='hi!'), AIMessage(content="what's up?")]
Buffer after setting return_messages to False:
<class 'str'>
Human: hi!
AI: what's up?
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Issue: we have several helper functions to import third-party libraries
like tools.gmail.utils.import_google in
[community.tools](https://api.python.langchain.com/en/latest/community_api_reference.html#id37).
And we have core.utils.utils.guard_import that works exactly for this
purpose.
The import_<package> functions work inconsistently and rather be private
functions.
Change: replaced these functions with the guard_import function.
Related to #21133
Issues (nit):
1. `utils.guard_import` prints wrong error message when there is an
import `error.` It prints the whole `module_name` but should be only the
first part as the pip package name. E.i. `langchain_core.utils` -> print
not `langchain-core` but `langchain_core.utils`. Also replace '_' with
'-' in the pip package name.
2. it does not handle the `ModuleNotFoundError` which raised if
`guard_import("wrong_module")`
Fixed issues; added ut-s. Controversial: I've reraised
`ModuleNotFoundError` as `ImportError`, since in case of the error, the
proposed action is the same - we need to install a missed package.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Issue: `load_summarize_chain` is placed in the __init__.py file. As a
result, it doesn't listed in the API Reference docs.
Change: moved code from __init__.py into a new file.
**PR message**:
- **Description:** Corrected a syntax error in the code comments within
the `create_tool_calling_agent` function in the langchain package.
- **Issue:** N/A
- **Dependencies:** No additional dependencies required.
- **Twitter handle:** N/A
This PR fixes#21196.
The error was occurring when calling chat completion API with a chat
history. Indeed, the Mistral API does not accept both `content` and
`tool_calls` in the same body.
This PR removes one of theses variables depending on the necessity.
---------
Co-authored-by: Maxime Perrin <mperrin@doing.fr>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
* Introduce individual `fetch_` methods for easier typing.
* Rework some docstrings to google style
* Move some logic to the tool
* Merge the 2 cassandra utility files
- support two-tuples of any sequence type (eg. json.loads never produces
tuples)
- support type alias for role key
- if id is passed in in dict form use it
- if tool_calls passed in in dict form use them
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Refactors the docs build in order to:
- run the same `make build` command in both vercel and local build
- incrementally build artifacts in 2 distinct steps, instead of building
all docs in-place (in vercel) or in a _dist dir (locally)
Highlights:
- introduces `make build` in order to build the docs
- collects and generates all files for the build in
`docs/build/intermediate`
- renders those jupyter notebook + markdown files into
`docs/build/outputs`
And now the outputs to host are in `docs/build/outputs`, which will need
a vercel settings change.
Todo:
- [ ] figure out how to point the right directory (right now deleting
and moving docs dir in vercel_build.sh isn't great)
**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
https://twitter.com/Rememberizer
## Summary
`ruff /path/to/file.py` works but is deprecated, and we now recommend
`ruff check /path/to/file.py` (to match `ruff format /path/to/file.py`).
Vertex DIY RAG APIs helps to build complex RAG systems and provide more
granular control, and are suited for custom use cases.
The Ranking API takes in a list of documents and reranks those documents
based on how relevant the documents are to a given query. Compared to
embeddings that look purely at the semantic similarity of a document and
a query, the ranking API can give you a more precise score for how well
a document answers a given query.
[Reference](https://cloud.google.com/generative-ai-app-builder/docs/ranking)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Sync the config in `devcontainer.json` and `docker-compose.yml`**
Issue: when opening the current `master` branch in a dev container in VS
Code, I get the following message as VS Code cannot find the mounted
source folder:
Opening in a GitHub Codespace works (it seems to ignore the mounts in
the `docker-compose.yml`.
This PR updates the mount in `docker-compose.yml` and the config in
`devcontainer.json` so that the two align.
I have tested these changes in GitHub Codespaces and a VS Code dev
container and both loaded successfully.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Add tests to check API keys and Active Directory tokens
are masked
**Issue:** Resolves#12165 for OpenAI and Azure OpenAI models
**Dependencies:** None
Also resolves#12473 which may be closed.
Additional contributors @alex4321 (#12473) and @onesolpark (#12542)
- [ ] **PR message**:
- **Description:** Refactored the lazy_load method to use asynchronous
execution for improved performance. The method now initiates scraping of
all URLs simultaneously using asyncio.gather, enhancing data fetching
efficiency. Each Document object is yielded immediately once its content
becomes available, streamlining the entire process.
- **Issue:** N/A
- **Dependencies:** Requires the asyncio library for handling
asynchronous tasks, which should already be part of standard Python
libraries in Python 3.7 and above.
- **Email:** [r73327118@gmail.com](mailto:r73327118@gmail.com)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Update python.py（experimental：Added code for PythonREPL）
Added code for PythonREPL, defining a static method 'sanitize_input'
that takes the string 'query' as input and returns a sanitizing string.
The purpose of this method is to remove unwanted characters from the
input string, Specifically:
1. Delete the whitespace at the beginning and end of the string (' \s').
2. Remove the quotation marks (`` ` ``) at the beginning and end of the
string.
3. Remove the keyword "python" at the beginning of the string (case
insensitive) because the user may have typed it.
This method uses regular expressions (regex) to implement sanitizing.
It all started with this code：
from langchain.agents import Tool
from langchain_experimental.utilities import PythonREPL
python_repl = PythonREPL()
repl_tool = Tool(
name="python_repl",
description="Remove redundant formatting marks at the beginning and end
of source code from input.Use a Python shell to execute python commands.
If you want to see the output of a value, you should print it out with
`print(...)`.",
func=python_repl.run,
)
When I call the agent to write a piece of code for me and execute it
with the defined code, I must get an error: SyntaxError('invalid
syntax', ('<string>', 1, 1,'In', 1, 2))
After checking, I found that pythonREPL has less formatting of input
code than the soon-to-be deprecated pythonREPL tool, so I added this
step to it, so that no matter what code I ask the agent to write for me,
it can be executed smoothly and get the output result.
I have tried modifying the prompt words to solve this problem before,
but it did not work, and by adding a simple format check, the problem is
well resolved.
<img width="1271" alt="image"
src="https://github.com/langchain-ai/langchain/assets/164149097/c49a685f-d246-4b11-b655-fd952fc2f04c">
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
This pull request updates the Bagel Network package name from
"betabageldb" to "bagelML" to align with the latest changes made by the
Bagel Network team.
The following modifications have been made:
- Updated all references to the old package name ("betabageldb") with
the new package name ("bagelML") throughout the codebase.
- Modified the documentation, and any relevant scripts to reflect the
package name change.
- Tested the changes to ensure that the functionality remains intact and
no breaking changes were introduced.
By merging this pull request, our project will stay up to date with the
latest Bagel Network package naming convention, ensuring compatibility
and smooth integration with their updated library.
Please review the changes and provide any feedback or suggestions. Thank
you!
**Description:** Update UpstageLayoutAnalysisParser and Loader and add
upstage loader example in pdf section
**Dependencies:** langchain_community
**Twitter handle:** [@upstageai](https://twitter.com/upstageai)
- [x] **Add tests and docs**: 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. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
**Issue:**
Currently `AzureSearch` vector store does not implement `delete` method.
This PR implements it. This also makes it compatible with LangChain
indexer.
**Dependencies:**
None
**Twitter handle:**
@martintriska1
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Upgrades prompts module to use optional imports.
This code was generated with a migration script, but had to be adjusted
manually a bit.
Testing in preparation for applying this code modification across the
rest of the modules in langchain package to reverse the dependency
between langchain community and langchain.
@@ -10,7 +10,7 @@ You can use the dev container configuration in this folder to build and run the
You may use the button above, or follow these steps to open this repo in a Codespace:
1. Click the **Code** drop-down menu at the top of https://github.com/langchain-ai/langchain.
1. Click on the **Codespaces** tab.
1. Click **Create codespace on master**.
1. Click **Create codespace on master**.
For more info, check out the [GitHub documentation](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace#creating-a-codespace).
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
[](https://codespaces.new/langchain-ai/langchain)
[](https://star-history.com/#langchain-ai/langchain)
For these applications, LangChain simplifies the entire application lifecycle:
- **Open-source libraries**: Build your applications using LangChain's [modular building blocks](https://python.langchain.com/docs/expression_language/) and [components](https://python.langchain.com/docs/modules/). Integrate with hundreds of [third-party providers](https://python.langchain.com/docs/integrations/platforms/).
- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://python.langchain.com/docs/langsmith/) so that you can constantly optimize and deploy with confidence.
- **Deployment**: Turn any chain into a REST API with [LangServe](https://python.langchain.com/docs/langserve).
- **Open-source libraries**: Build your applications using LangChain's open-source [building blocks](https://python.langchain.com/v0.2/docs/concepts#langchain-expression-language-lcel), [components](https://python.langchain.com/v0.2/docs/concepts), and [third-party integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/).
Use [LangGraph](/docs/concepts/#langgraph) to build stateful agents with first-class streaming and human-in-the-loop support.
- **Productionization**: Inspect, monitor, and evaluate your apps with [LangSmith](https://docs.smith.langchain.com/) so that you can constantly optimize and deploy with confidence.
- **Deployment**: Turn your LangGraph applications into production-ready APIs and Assistants with [LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/).
### Open-source libraries
- **`langchain-core`**: Base abstractions and LangChain Expression Language.
- **`langchain-community`**: Third party integrations.
- Some integrations have been further split into **partner packages** that only rely on **`langchain-core`**. Examples include **`langchain_openai`** and **`langchain_anthropic`**.
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
- **[`LangGraph`](https://python.langchain.com/docs/langgraph)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
- **[`LangGraph`](https://langchain-ai.github.io/langgraph/)**: A library for building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Integrates smoothly with LangChain, but can be used without it.
### Productionization:
- **[LangSmith](https://python.langchain.com/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
- **[LangSmith](https://docs.smith.langchain.com/)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
### Deployment:
- **[LangServe](https://python.langchain.com/docs/langserve)**: A library for deploying LangChain chains as REST APIs.
- **[LangGraph Cloud](https://langchain-ai.github.io/langgraph/cloud/)**: Turn your LangGraph applications into production-ready APIs and Assistants.
- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
And much more! Head to the [Use cases](https://python.langchain.com/docs/use_cases/) section of the docs for more.
And much more! Head to the [Tutorials](https://python.langchain.com/v0.2/docs/tutorials/) section of the docs for more.
## 🚀 How does LangChain help?
The main value props of the LangChain libraries are:
@@ -87,49 +88,49 @@ Off-the-shelf chains make it easy to get started. Components make it easy to cus
LCEL is the foundation of many of LangChain's components, and is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
- **[Overview](https://python.langchain.com/docs/expression_language/)**: LCEL and its benefits
- **[Interface](https://python.langchain.com/docs/expression_language/interface)**: The standard interface for LCEL objects
- **[Primitives](https://python.langchain.com/docs/expression_language/primitives)**: More on the primitives LCEL includes
- **[Overview](https://python.langchain.com/v0.2/docs/concepts/#langchain-expression-language-lcel)**: LCEL and its benefits
- **[Interface](https://python.langchain.com/v0.2/docs/concepts/#runnable-interface)**: The standard Runnable interface for LCEL objects
- **[Primitives](https://python.langchain.com/v0.2/docs/how_to/#langchain-expression-language-lcel)**: More on the primitives LCEL includes
- **[Cheatsheet](https://python.langchain.com/v0.2/docs/how_to/lcel_cheatsheet/)**: Quick overview of the most common usage patterns
## Components
Components fall into the following **modules**:
**📃 Model I/O:**
**📃 Model I/O**
This includes [prompt management](https://python.langchain.com/docs/modules/model_io/prompts/), [prompt optimization](https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/), a generic interface for [chat models](https://python.langchain.com/docs/modules/model_io/chat/) and [LLMs](https://python.langchain.com/docs/modules/model_io/llms/), and common utilities for working with [model outputs](https://python.langchain.com/docs/modules/model_io/output_parsers/).
This includes [prompt management](https://python.langchain.com/v0.2/docs/concepts/#prompt-templates), [prompt optimization](https://python.langchain.com/v0.2/docs/concepts/#example-selectors), a generic interface for [chat models](https://python.langchain.com/v0.2/docs/concepts/#chat-models) and [LLMs](https://python.langchain.com/v0.2/docs/concepts/#llms), and common utilities for working with [model outputs](https://python.langchain.com/v0.2/docs/concepts/#output-parsers).
**📚 Retrieval:**
**📚 Retrieval**
Retrieval Augmented Generation involves [loading data](https://python.langchain.com/docs/modules/data_connection/document_loaders/) from a variety of sources, [preparing it](https://python.langchain.com/docs/modules/data_connection/document_loaders/), [then retrieving it](https://python.langchain.com/docs/modules/data_connection/retrievers/) for use in the generation step.
Retrieval Augmented Generation involves [loading data](https://python.langchain.com/v0.2/docs/concepts/#document-loaders) from a variety of sources, [preparing it](https://python.langchain.com/v0.2/docs/concepts/#text-splitters), then [searching over (a.k.a. retrieving from)](https://python.langchain.com/v0.2/docs/concepts/#retrievers) it for use in the generation step.
**🤖 Agents:**
**🤖 Agents**
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete done. LangChain provides a [standard interface for agents](https://python.langchain.com/docs/modules/agents/), a [selection of agents](https://python.langchain.com/docs/modules/agents/agent_types/) to choose from, and examples of end-to-end agents.
Agents allow an LLM autonomy over how a task is accomplished. Agents make decisions about which Actions to take, then take that Action, observe the result, and repeat until the task is complete. LangChain provides a [standard interface for agents](https://python.langchain.com/v0.2/docs/concepts/#agents), along with [LangGraph](https://github.com/langchain-ai/langgraph) for building custom agents.
## 📖 Documentation
Please see [here](https://python.langchain.com) for full documentation, which includes:
- [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples
- [Use case](https://python.langchain.com/docs/use_cases/) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/)
-Overviews of the [interfaces](https://python.langchain.com/docs/expression_language/), [components](https://python.langchain.com/docs/modules/), and [integrations](https://python.langchain.com/docs/integrations/providers)
You can also check out the full [API Reference docs](https://api.python.langchain.com).
- [Introduction](https://python.langchain.com/v0.2/docs/introduction/): Overview of the framework and the structure of the docs.
- [Tutorials](https://python.langchain.com/docs/use_cases/): If you're looking to build something specific or are more of a hands-on learner, check out our tutorials. This is the best place to get started.
-[How-to guides](https://python.langchain.com/v0.2/docs/how_to/): Answers to “How do I….?” type questions. These guides are goal-oriented and concrete; they're meant to help you complete a specific task.
- [Conceptual guide](https://python.langchain.com/v0.2/docs/concepts/): Conceptual explanations of the key parts of the framework.
- [API Reference](https://api.python.langchain.com): Thorough documentation of every class and method.
## 🌐 Ecosystem
- [🦜🛠️ LangSmith](https://python.langchain.com/docs/langsmith/): Tracing and evaluating your language model applications and intelligent agents to help you move from prototype to production.
- [🦜🕸️ LangGraph](https://python.langchain.com/docs/langgraph): Creating stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploying LangChain runnables and chains as REST APIs.
- [LangChain Templates](https://python.langchain.com/docs/templates/): Example applications hosted with LangServe.
- [🦜🛠️ LangSmith](https://docs.smith.langchain.com/): Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
- [🦜🕸️ LangGraph](https://langchain-ai.github.io/langgraph/): Create stateful, multi-actor applications with LLMs. Integrates smoothly with LangChain, but can be used without it.
- [🦜🏓 LangServe](https://python.langchain.com/docs/langserve): Deploy LangChain runnables and chains as REST APIs.
## 💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
For detailed information on how to contribute, see [here](https://python.langchain.com/v0.2/docs/contributing/).
[two_agent_debate_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_agent_debate_tools.ipynb) | Simulate multi-agent dialogues where the agents can utilize various tools.
[two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialogue simulator class is used to coordinate the dialogue between the protagonist and the dungeon master.
[wikibase_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/wikibase_agent.ipynb) | Create a simple wikibase agent that utilizes sparql generation, with testing done on http://wikidata.org.
[oracleai_demo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/oracleai_demo.ipynb) | This guide outlines how to utilize Oracle AI Vector Search alongside Langchain for an end-to-end RAG pipeline, providing step-by-step examples. The process includes loading documents from various sources using OracleDocLoader, summarizing them either within or outside the database with OracleSummary, and generating embeddings similarly through OracleEmbeddings. It also covers chunking documents according to specific requirements using Advanced Oracle Capabilities from OracleTextSplitter, and finally, storing and indexing these documents in a Vector Store for querying with OracleVS.
"Many documents contain a mixture of content types, including text and images. \n",
"\n",
"Yet, information captured in images is lost in most RAG applications.\n",
"\n",
"With the emergence of multimodal LLMs, like [GPT-4V](https://openai.com/research/gpt-4v-system-card), it is worth considering how to utilize images in RAG:\n",
"\n",
"In this demo we\n",
"\n",
"* Use multimodal embeddings from Nomic Embed [Vision](https://huggingface.co/nomic-ai/nomic-embed-vision-v1.5) and [Text](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) to embed images and text\n",
"* Retrieve both using similarity search\n",
"* Pass raw images and text chunks to a multimodal LLM for answer synthesis \n",
"\n",
"## Signup\n",
"\n",
"Get your API token, then run:\n",
"```\n",
"! nomic login\n",
"```\n",
"\n",
"Then run with your generated API token \n",
"```\n",
"! nomic login < token > \n",
"```\n",
"\n",
"## Packages\n",
"\n",
"For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
"Let's look at an example pdfs containing interesting images.\n",
"\n",
"1/ Art from the J Paul Getty museum:\n",
"\n",
" * Here is a [zip file](https://drive.google.com/file/d/18kRKbq2dqAhhJ3DfZRnYcTBEUfYxe1YR/view?usp=sharing) with the PDF and the already extracted images. \n",
"We can use `partition_pdf` below from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images.\n",
"\n",
"To supply this to extract the images:\n",
"```\n",
"extract_images_in_pdf=True\n",
"```\n",
"\n",
"\n",
"\n",
"If using this zip file, then you can simply process the text only with:\n",
"# Oracle AI Vector Search with Document Processing\n",
"Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords.\n",
"One of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system.\n",
"This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems.\n",
"\n",
"In addition, your vectors can benefit from all of Oracle Database’s most powerful features, like the following:\n",
"This guide demonstrates how Oracle AI Vector Search can be used with Langchain to serve an end-to-end RAG pipeline. This guide goes through examples of:\n",
"\n",
" * Loading the documents from various sources using OracleDocLoader\n",
" * Summarizing them within/outside the database using OracleSummary\n",
" * Generating embeddings for them within/outside the database using OracleEmbeddings\n",
" * Chunking them according to different requirements using Advanced Oracle Capabilities from OracleTextSplitter\n",
" * Storing and Indexing them in a Vector Store and querying them for queries in OracleVS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are just starting with Oracle Database, consider exploring the [free Oracle 23 AI](https://www.oracle.com/database/free/#resources) which provides a great introduction to setting up your database environment. While working with the database, it is often advisable to avoid using the system user by default; instead, you can create your own user for enhanced security and customization. For detailed steps on user creation, refer to our [end-to-end guide](https://github.com/langchain-ai/langchain/blob/master/cookbook/oracleai_demo.ipynb) which also shows how to set up a user in Oracle. Additionally, understanding user privileges is crucial for managing database security effectively. You can learn more about this topic in the official [Oracle guide](https://docs.oracle.com/en/database/oracle/oracle-database/19/admqs/administering-user-accounts-and-security.html#GUID-36B21D72-1BBB-46C9-A0C9-F0D2A8591B8D) on administering user accounts and security."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prerequisites\n",
"\n",
"Please install Oracle Python Client driver to use Langchain with Oracle AI Vector Search. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# pip install oracledb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Demo User\n",
"First, create a demo user with all the required privileges. "
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection successful!\n",
"User setup done!\n"
]
}
],
"source": [
"import sys\n",
"\n",
"import oracledb\n",
"\n",
"# Update with your username, password, hostname, and service_name\n",
" print(f\"User setup failed with error: {e}\")\n",
" finally:\n",
" cursor.close()\n",
" conn.close()\n",
"except Exception as e:\n",
" print(f\"Connection failed with error: {e}\")\n",
" sys.exit(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process Documents using Oracle AI\n",
"Consider the following scenario: users possess documents stored either in an Oracle Database or a file system and intend to utilize this data with Oracle AI Vector Search powered by Langchain.\n",
"\n",
"To prepare the documents for analysis, a comprehensive preprocessing workflow is necessary. Initially, the documents must be retrieved, summarized (if required), and chunked as needed. Subsequent steps involve generating embeddings for these chunks and integrating them into the Oracle AI Vector Store. Users can then conduct semantic searches on this data.\n",
"\n",
"The Oracle AI Vector Search Langchain library encompasses a suite of document processing tools that facilitate document loading, chunking, summary generation, and embedding creation.\n",
"\n",
"In the sections that follow, we will detail the utilization of Oracle AI Langchain APIs to effectively implement each of these processes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to Demo User\n",
"The following sample code will show how to connect to Oracle Database. By default, python-oracledb runs in a ‘Thin’ mode which connects directly to Oracle Database. This mode does not need Oracle Client libraries. However, some additional functionality is available when python-oracledb uses them. Python-oracledb is said to be in ‘Thick’ mode when Oracle Client libraries are used. Both modes have comprehensive functionality supporting the Python Database API v2.0 Specification. See the following [guide](https://python-oracledb.readthedocs.io/en/latest/user_guide/appendix_a.html#featuresummary) that talks about features supported in each mode. You might want to switch to thick-mode if you are unable to use thin-mode."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connection successful!\n"
]
}
],
"source": [
"import sys\n",
"\n",
"import oracledb\n",
"\n",
"# please update with your username, password, hostname and service_name\n",
" create_table_sql = \"\"\"create table demo_tab (id number, data clob)\"\"\"\n",
" cursor.execute(create_table_sql)\n",
"\n",
" insert_row_sql = \"\"\"insert into demo_tab values (:1, :2)\"\"\"\n",
" rows_to_insert = [\n",
" (\n",
" 1,\n",
" \"If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\",\n",
" ),\n",
" (\n",
" 2,\n",
" \"A tablespace can be online (accessible) or offline (not accessible) whenever the database is open.\\nA tablespace is usually online so that its data is available to users. The SYSTEM tablespace and temporary tablespaces cannot be taken offline.\",\n",
" ),\n",
" (\n",
" 3,\n",
" \"The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table.\\nSometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\",\n",
"With the inclusion of a demo user and a populated sample table, the remaining configuration involves setting up embedding and summary functionalities. Users are presented with multiple provider options, including local database solutions and third-party services such as Ocigenai, Hugging Face, and OpenAI. Should users opt for a third-party provider, they are required to establish credentials containing the necessary authentication details. Conversely, if selecting a database as the provider for embeddings, it is necessary to upload an ONNX model to the Oracle Database. No additional setup is required for summary functionalities when using the database option."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load ONNX Model\n",
"\n",
"Oracle accommodates a variety of embedding providers, enabling users to choose between proprietary database solutions and third-party services such as OCIGENAI and HuggingFace. This selection dictates the methodology for generating and managing embeddings.\n",
"\n",
"***Important*** : Should users opt for the database option, they must upload an ONNX model into the Oracle Database. Conversely, if a third-party provider is selected for embedding generation, uploading an ONNX model to Oracle Database is not required.\n",
"\n",
"A significant advantage of utilizing an ONNX model directly within Oracle is the enhanced security and performance it offers by eliminating the need to transmit data to external parties. Additionally, this method avoids the latency typically associated with network or REST API calls.\n",
"\n",
"Below is the example code to upload an ONNX model into Oracle Database:"
"When selecting third-party providers for generating embeddings, users are required to establish credentials to securely access the provider's endpoints.\n",
"\n",
"***Important:*** No credentials are necessary when opting for the 'database' provider to generate embeddings. However, should users decide to utilize a third-party provider, they must create credentials specific to the chosen provider.\n",
"Users have the flexibility to load documents from either the Oracle Database, a file system, or both, by appropriately configuring the loader parameters. For comprehensive details on these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-73397E89-92FB-48ED-94BB-1AD960C4EA1F).\n",
"\n",
"A significant advantage of utilizing OracleDocLoader is its capability to process over 150 distinct file formats, eliminating the need for multiple loaders for different document types. For a complete list of the supported formats, please refer to the [Oracle Text Supported Document Formats](https://docs.oracle.com/en/database/oracle/oracle-database/23/ccref/oracle-text-supported-document-formats.html).\n",
"\n",
"Below is a sample code snippet that demonstrates how to use OracleDocLoader"
"Now that the user loaded the documents, they may want to generate a summary for each document. The Oracle AI Vector Search Langchain library offers a suite of APIs designed for document summarization. It supports multiple summarization providers such as Database, OCIGENAI, HuggingFace, among others, allowing users to select the provider that best meets their needs. To utilize these capabilities, users must configure the summary parameters as specified. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-EC9DDB58-6A15-4B36-BA66-ECBA20D2CE57)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Note:*** The users may need to set proxy if they want to use some 3rd party summary generation providers other than Oracle's in-house and default provider: 'database'. If you don't have proxy, please remove the proxy parameter when you instantiate the OracleSummary."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# proxy to be used when we instantiate summary and embedder object\n",
"proxy = \"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following sample code will show how to generate summary:"
"The documents may vary in size, ranging from small to very large. Users often prefer to chunk their documents into smaller sections to facilitate the generation of embeddings. A wide array of customization options is available for this splitting process. For comprehensive details regarding these parameters, please consult the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-4E145629-7098-4C7C-804F-FC85D1F24240).\n",
"\n",
"Below is a sample code illustrating how to implement this:"
"Now that the documents are chunked as per requirements, the users may want to generate embeddings for these chunks. Oracle AI Vector Search provides multiple methods for generating embeddings, utilizing either locally hosted ONNX models or third-party APIs. For comprehensive instructions on configuring these alternatives, please refer to the [Oracle AI Vector Search Guide](https://docs.oracle.com/en/database/oracle/oracle-database/23/arpls/dbms_vector_chain1.html#GUID-C6439E94-4E86-4ECD-954E-4B73D53579DE)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***Note:*** Users may need to configure a proxy to utilize third-party embedding generation providers, excluding the 'database' provider that utilizes an ONNX model."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# proxy to be used when we instantiate summary and embedder object\n",
"proxy = \"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following sample code will show how to generate embeddings:"
"Now that you know how to use Oracle AI Langchain library APIs individually to process the documents, let us show how to integrate with Oracle AI Vector Store to facilitate the semantic searches."
"print(f\"Vector Store Table: {vectorstore.table_name}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example provided illustrates the creation of a vector store using the DOT_PRODUCT distance strategy. Users have the flexibility to employ various distance strategies with the Oracle AI Vector Store, as detailed in our [comprehensive guide](https://python.langchain.com/v0.1/docs/integrations/vectorstores/oracle/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With embeddings now stored in vector stores, it is advisable to establish an index to enhance semantic search performance during query execution.\n",
"\n",
"***Note*** Should you encounter an \"insufficient memory\" error, it is recommended to increase the ***vector_memory_size*** in your database configuration\n",
"\n",
"Below is a sample code snippet for creating an index:"
"This example demonstrates the creation of a default HNSW index on embeddings within the 'oravs' table. Users may adjust various parameters according to their specific needs. For detailed information on these parameters, please consult the [Oracle AI Vector Search Guide book](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/manage-different-categories-vector-indexes.html).\n",
"\n",
"Additionally, various types of vector indices can be created to meet diverse requirements. More details can be found in our [comprehensive guide](https://python.langchain.com/v0.1/docs/integrations/vectorstores/oracle/).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform Semantic Search\n",
"All set!\n",
"\n",
"We have successfully processed the documents and stored them in the vector store, followed by the creation of an index to enhance query performance. We are now prepared to proceed with semantic searches.\n",
"\n",
"Below is the sample code for this process:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Document(page_content='The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table. Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.', metadata={'_oid': '662f2f257677f3c2311a8ff999fd34e5', '_rowid': 'AAAR/xAAEAAAAAnAAC', 'id': '662f2f257677f3c2311a8ff999fd34e5$3$1', 'document_id': '3', 'document_summary': 'Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\\n\\n'})]\n",
"[]\n",
"[(Document(page_content='The database stores LOBs differently from other data types. Creating a LOB column implicitly creates a LOB segment and a LOB index. The tablespace containing the LOB segment and LOB index, which are always stored together, may be different from the tablespace containing the table. Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.', metadata={'_oid': '662f2f257677f3c2311a8ff999fd34e5', '_rowid': 'AAAR/xAAEAAAAAnAAC', 'id': '662f2f257677f3c2311a8ff999fd34e5$3$1', 'document_id': '3', 'document_summary': 'Sometimes the database can store small amounts of LOB data in the table itself rather than in a separate LOB segment.\\n\\n'}), 0.055675752460956573)]\n",
"[]\n",
"[Document(page_content='If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.', metadata={'_oid': '662f2f253acf96b33b430b88699490a2', '_rowid': 'AAAR/xAAEAAAAAnAAA', 'id': '662f2f253acf96b33b430b88699490a2$1$1', 'document_id': '1', 'document_summary': 'If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\\n\\n'})]\n",
"[Document(page_content='If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.', metadata={'_oid': '662f2f253acf96b33b430b88699490a2', '_rowid': 'AAAR/xAAEAAAAAnAAA', 'id': '662f2f253acf96b33b430b88699490a2$1$1', 'document_id': '1', 'document_summary': 'If the answer to any preceding questions is yes, then the database stops the search and allocates space from the specified tablespace; otherwise, space is allocated from the database default shared temporary tablespace.\\n\\n'})]\n"
"] += f\". Valid values are {sorted(latest_price['starrating'].value_counts().index.tolist())}\"\n",
"attribute_info[3][\n",
" \"description\"\n",
"] += f\". Valid values are {sorted(latest_price['maxoccupancy'].value_counts().index.tolist())}\"\n",
"attribute_info[-3][\n",
" \"description\"\n",
"] += f\". Valid values are {sorted(latest_price['country'].value_counts().index.tolist())}\""
"attribute_info[-2][\"description\"] += (\n",
" f\". Valid values are {sorted(latest_price['starrating'].value_counts().index.tolist())}\"\n",
")\n",
"attribute_info[3][\"description\"] += (\n",
" f\". Valid values are {sorted(latest_price['maxoccupancy'].value_counts().index.tolist())}\"\n",
")\n",
"attribute_info[-3][\"description\"] += (\n",
" f\". Valid values are {sorted(latest_price['country'].value_counts().index.tolist())}\"\n",
")"
]
},
{
@@ -688,9 +688,9 @@
"metadata": {},
"outputs": [],
"source": [
"attribute_info[-3][\n",
" \"description\"\n",
"] += \". NOTE: Only use the 'eq' operator if a specific country is mentioned. If a region is mentioned, include all relevant countries in filter.\"\n",
"attribute_info[-3][\"description\"] += (\n",
" \". NOTE: Only use the 'eq' operator if a specific country is mentioned. If a region is mentioned, include all relevant countries in filter.\"\n",
..NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
..NOTE:: {{objname}} implements the standard :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>`. 🏃
The :py:class:`Runnable Interface <langchain_core.runnables.base.Runnable>` has additional methods that are available on runnables, such as :py:meth:`with_types <langchain_core.runnables.base.Runnable.with_types>`, :py:meth:`with_retry <langchain_core.runnables.base.Runnable.with_retry>`, :py:meth:`assign <langchain_core.runnables.base.Runnable.assign>`, :py:meth:`bind <langchain_core.runnables.base.Runnable.bind>`, :py:meth:`get_graph <langchain_core.runnables.base.Runnable.get_graph>`, and more.
| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al. | 2024-01-08 | `Cookbook:` [together_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb)
| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen, et al. | 2023-12-11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval)
| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al. | 2023-11-15 | `Template:` [chain-of-note-wiki](https://python.langchain.com/docs/templates/chain-of-note-wiki)
| `2310.11511v1` [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](http://arxiv.org/abs/2310.11511v1) | Akari Asai, Zeqiu Wu, Yizhong Wang, et al. | 2023-10-17 | `Cookbook:` [langgraph_self_rag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_self_rag.ipynb)
| `2310.06117v2` [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](http://arxiv.org/abs/2310.06117v2) | Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, et al. | 2023-10-09 | `Template:` [stepback-qa-prompting](https://python.langchain.com/docs/templates/stepback-qa-prompting), `Cookbook:` [stepback-qa](https://github.com/langchain-ai/langchain/blob/master/cookbook/stepback-qa.ipynb)
| `2307.09288v2` [Llama 2: Open Foundation and Fine-Tuned Chat Models](http://arxiv.org/abs/2307.09288v2) | Hugo Touvron, Louis Martin, Kevin Stone, et al. | 2023-07-18 | `Cookbook:` [Semi_Structured_RAG](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb)
| `2305.14283v3` [Query Rewriting for Retrieval-Augmented Large Language Models](http://arxiv.org/abs/2305.14283v3) | Xinbei Ma, Yeyun Gong, Pengcheng He, et al. | 2023-05-23 | `Template:` [rewrite-retrieve-read](https://python.langchain.com/docs/templates/rewrite-retrieve-read), `Cookbook:` [rewrite](https://github.com/langchain-ai/langchain/blob/master/cookbook/rewrite.ipynb)
| `2305.08291v1` [Large Language Model Guided Tree-of-Thought](http://arxiv.org/abs/2305.08291v1) | Jieyi Long | 2023-05-15 | `API:` [langchain_experimental.tot](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.tot), `Cookbook:` [tree_of_thought](https://github.com/langchain-ai/langchain/blob/master/cookbook/tree_of_thought.ipynb)
| `2305.04091v3` [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](http://arxiv.org/abs/2305.04091v3) | Lei Wang, Wanyu Xu, Yihuai Lan, et al. | 2023-05-06 | `Cookbook:` [plan_and_execute_agent](https://github.com/langchain-ai/langchain/blob/master/cookbook/plan_and_execute_agent.ipynb)
| `2304.03442v2` [Generative Agents: Interactive Simulacra of Human Behavior](http://arxiv.org/abs/2304.03442v2) | Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, et al. | 2023-04-07 | `Cookbook:` [multiagent_bidding](https://github.com/langchain-ai/langchain/blob/master/cookbook/multiagent_bidding.ipynb), [generative_agents_interactive_simulacra_of_human_behavior](https://github.com/langchain-ai/langchain/blob/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb)
| `2303.17760v2` [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](http://arxiv.org/abs/2303.17760v2) | Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al. | 2023-03-31 | `Cookbook:` [camel_role_playing](https://github.com/langchain-ai/langchain/blob/master/cookbook/camel_role_playing.ipynb)
| `2303.17580v4` [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](http://arxiv.org/abs/2303.17580v4) | Yongliang Shen, Kaitao Song, Xu Tan, et al. | 2023-03-30 | `API:` [langchain_experimental.autonomous_agents](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.autonomous_agents), `Cookbook:` [hugginggpt](https://github.com/langchain-ai/langchain/blob/master/cookbook/hugginggpt.ipynb)
| `2303.08774v6` [GPT-4 Technical Report](http://arxiv.org/abs/2303.08774v6) | OpenAI, Josh Achiam, Steven Adler, et al. | 2023-03-15 | `Docs:` [docs/integrations/vectorstores/mongodb_atlas](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas)
| `2301.10226v4` [A Watermark for Large Language Models](http://arxiv.org/abs/2301.10226v4) | John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al. | 2023-01-24 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...OCIModelDeploymentTGI](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI.html#langchain_community.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2212.10496v1` [Precise Zero-Shot Dense Retrieval without Relevance Labels](http://arxiv.org/abs/2212.10496v1) | Luyu Gao, Xueguang Ma, Jimmy Lin, et al. | 2022-12-20 | `API:` [langchain...HypotheticalDocumentEmbedder](https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html#langchain.chains.hyde.base.HypotheticalDocumentEmbedder), `Template:` [hyde](https://python.langchain.com/docs/templates/hyde), `Cookbook:` [hypothetical_document_embeddings](https://github.com/langchain-ai/langchain/blob/master/cookbook/hypothetical_document_embeddings.ipynb)
| `2212.07425v3` [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](http://arxiv.org/abs/2212.07425v3) | Zhivar Sourati, Vishnu Priya Prasanna Venkatesh, Darshan Deshpande, et al. | 2022-12-12 | `API:` [langchain_experimental.fallacy_removal](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.fallacy_removal)
| `2211.13892v2` [Complementary Explanations for Effective In-Context Learning](http://arxiv.org/abs/2211.13892v2) | Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al. | 2022-11-25 | `API:` [langchain_core...MaxMarginalRelevanceExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html#langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector)
| `2211.10435v2` [PAL: Program-aided Language Models](http://arxiv.org/abs/2211.10435v2) | Luyu Gao, Aman Madaan, Shuyan Zhou, et al. | 2022-11-18 | `API:` [langchain_experimental...PALChain](https://api.python.langchain.com/en/latest/pal_chain/langchain_experimental.pal_chain.base.PALChain.html#langchain_experimental.pal_chain.base.PALChain), [langchain_experimental.pal_chain](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.pal_chain), `Cookbook:` [program_aided_language_model](https://github.com/langchain-ai/langchain/blob/master/cookbook/program_aided_language_model.ipynb)
| `2210.03629v3` [ReAct: Synergizing Reasoning and Acting in Language Models](http://arxiv.org/abs/2210.03629v3) | Shunyu Yao, Jeffrey Zhao, Dian Yu, et al. | 2022-10-06 | `Docs:` [docs/integrations/providers/cohere](https://python.langchain.com/docs/integrations/providers/cohere), [docs/integrations/chat/huggingface](https://python.langchain.com/docs/integrations/chat/huggingface), [docs/integrations/tools/ionic_shopping](https://python.langchain.com/docs/integrations/tools/ionic_shopping), `API:` [langchain...create_react_agent](https://api.python.langchain.com/en/latest/agents/langchain.agents.react.agent.create_react_agent.html#langchain.agents.react.agent.create_react_agent), [langchain...TrajectoryEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain.html#langchain.evaluation.agents.trajectory_eval_chain.TrajectoryEvalChain)
| `2209.10785v2` [Deep Lake: a Lakehouse for Deep Learning](http://arxiv.org/abs/2209.10785v2) | Sasun Hambardzumyan, Abhinav Tuli, Levon Ghukasyan, et al. | 2022-09-22 | `Docs:` [docs/integrations/providers/activeloop_deeplake](https://python.langchain.com/docs/integrations/providers/activeloop_deeplake)
| `2205.12654v1` [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](http://arxiv.org/abs/2205.12654v1) | Kevin Heffernan, Onur Çelebi, Holger Schwenk | 2022-05-25 | `API:` [langchain_community...LaserEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_community.embeddings.laser.LaserEmbeddings.html#langchain_community.embeddings.laser.LaserEmbeddings)
| `2204.00498v1` [Evaluating the Text-to-SQL Capabilities of Large Language Models](http://arxiv.org/abs/2204.00498v1) | Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau | 2022-03-15 | `API:` [langchain_community...SparkSQL](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.spark_sql.SparkSQL.html#langchain_community.utilities.spark_sql.SparkSQL), [langchain_community...SQLDatabase](https://api.python.langchain.com/en/latest/utilities/langchain_community.utilities.sql_database.SQLDatabase.html#langchain_community.utilities.sql_database.SQLDatabase)
| `2202.00666v5` [Locally Typical Sampling](http://arxiv.org/abs/2202.00666v5) | Clara Meister, Tiago Pimentel, Gian Wiher, et al. | 2022-02-01 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `2103.00020v1` [Learning Transferable Visual Models From Natural Language Supervision](http://arxiv.org/abs/2103.00020v1) | Alec Radford, Jong Wook Kim, Chris Hallacy, et al. | 2021-02-26 | `API:` [langchain_experimental.open_clip](https://api.python.langchain.com/en/latest/experimental_api_reference.html#module-langchain_experimental.open_clip)
| `1909.05858v2` [CTRL: A Conditional Transformer Language Model for Controllable Generation](http://arxiv.org/abs/1909.05858v2) | Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al. | 2019-09-11 | `API:` [langchain_community...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_huggingface...HuggingFaceEndpoint](https://api.python.langchain.com/en/latest/llms/langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint.html#langchain_huggingface.llms.huggingface_endpoint.HuggingFaceEndpoint), [langchain_community...HuggingFaceTextGenInference](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference.html#langchain_community.llms.huggingface_text_gen_inference.HuggingFaceTextGenInference)
| `1908.10084v1` [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](http://arxiv.org/abs/1908.10084v1) | Nils Reimers, Iryna Gurevych | 2019-08-27 | `Docs:` [docs/integrations/text_embedding/sentence_transformers](https://python.langchain.com/docs/integrations/text_embedding/sentence_transformers)
## Self-Discover: Large Language Models Self-Compose Reasoning Structures
- **arXiv id:** 2402.03620v1
- **Title:** Self-Discover: Large Language Models Self-Compose Reasoning Structures
- **Authors:** Pei Zhou, Jay Pujara, Xiang Ren, et al.
### Introduction to LangChain with Harrison Chase, creator of LangChain
- [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
- [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/@The_Full_Stack)
- [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)
### [Tutorials on YouTube](/docs/additional_resources/tutorials/#tutorials)
## Videos (sorted by views)
- [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain OpenAI API)](https://youtu.be/9AXP7tCI9PI) by [TechLead](https://www.youtube.com/@TechLead)
- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson)
- [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DaveShap)
- [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder)
- [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder)
- [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder)
- [How to Use Langchain With `Zapier` | Write and Send Email with GPT-3 | OpenAI API Tutorial](https://youtu.be/p9v2-xEa9A0) by [StarMorph AI](https://www.youtube.com/@starmorph)
- [Use Your Locally Stored Files To Get Response From GPT - `OpenAI` | Langchain | Python](https://youtu.be/NC1Ni9KS-rk) by [Shweta Lodha](https://www.youtube.com/@shweta-lodha)
- [`Langchain JS` | How to Use GPT-3, GPT-4 to Reference your own Data | `OpenAI Embeddings` Intro](https://youtu.be/veV2I-NEjaM) by [StarMorph AI](https://www.youtube.com/@starmorph)
- [The easiest way to work with large language models | Learn LangChain in 10min](https://youtu.be/kmbS6FDQh7c) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
- [4 Autonomous AI Agents: “Westworld” simulation `BabyAGI`, `AutoGPT`, `Camel`, `LangChain`](https://youtu.be/yWbnH6inT_U) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
- [AI CAN SEARCH THE INTERNET? Langchain Agents + OpenAI ChatGPT](https://youtu.be/J-GL0htqda8) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
- [Query Your Data with GPT-4 | Embeddings, Vector Databases | Langchain JS Knowledgebase](https://youtu.be/jRnUPUTkZmU) by [StarMorph AI](https://www.youtube.com/@starmorph)
- [`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)
- [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
- [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
- [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@heymichaeldaigler)
- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nickdaigler)
- [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast)
- [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods)
- [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
- [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI)
- [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest)
- [LangChain + `OpenAI` tutorial: Building a Q&A system w/ own text data](https://youtu.be/DYOU_Z0hAwo) by [Samuel Chan](https://www.youtube.com/@SamuelChan)
- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@heymichaeldaigler)
- [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)
- [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/@heymichaeldaigler)
- [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)
- [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 [Girlfriend GPT](https://www.youtube.com/@girlfriendGPT)
- [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)
- ⛓ [Vector Embeddings Tutorial – Code Your Own AI Assistant with `GPT-4 API` + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=5uJhxoh2tvdnOXok) by [FreeCodeCamp.org](https://www.youtube.com/@freecodecamp)
- ⛓ [Fully LOCAL `Llama 2` Q&A with LangChain](https://youtu.be/wgYctKFnQ74?si=UX1F3W-B3MqF4-K-) by [1littlecoder](https://www.youtube.com/@1littlecoder)
- ⛓ [Fully LOCAL `Llama 2` Langchain on CPU](https://youtu.be/yhECvKMu8kM?si=IvjxwlA1c09VwHZ4) by [1littlecoder](https://www.youtube.com/@1littlecoder)
- ⛓ [Build LangChain Audio Apps with Python in 5 Minutes](https://youtu.be/7w7ysaDz2W4?si=BvdMiyHhormr2-vr) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
- ⛓ [`Voiceflow` & `Flowise`: Want to Beat Competition? New Tutorial with Real AI Chatbot](https://youtu.be/EZKkmeFwag0?si=-4dETYDHEstiK_bb) by [AI SIMP](https://www.youtube.com/@aisimp)
- ⛓ [THIS Is How You Build Production-Ready AI Apps (`LangSmith` Tutorial)](https://youtu.be/tFXm5ijih98?si=lfiqpyaivxHFyI94) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
- ⛓ [Build POWERFUL LLM Bots EASILY with Your Own Data - `Embedchain` - Langchain 2.0? (Tutorial)](https://youtu.be/jE24Y_GasE8?si=0yEDZt3BK5Q-LIuF) by [WorldofAI](https://www.youtube.com/@intheworldofai)
- ⛓ [`Code Llama` powered Gradio App for Coding: Runs on CPU](https://youtu.be/AJOhV6Ryy5o?si=ouuQT6IghYlc1NEJ) by [AI Anytime](https://www.youtube.com/@AIAnytime)
- ⛓ [LangChain Complete Course in One Video | Develop LangChain (AI) Based Solutions for Your Business](https://youtu.be/j9mQd-MyIg8?si=_wlNT3nP2LpDKztZ) by [UBprogrammer](https://www.youtube.com/@UBprogrammer)
- ⛓ [How to Run `LLaMA` Locally on CPU or GPU | Python & Langchain & CTransformers Guide](https://youtu.be/SvjWDX2NqiM?si=DxFml8XeGhiLTzLV) by [Code With Prince](https://www.youtube.com/@CodeWithPrince)
- ⛓ [Prompt Engineering in Web Development | Using LangChain and Templates with OpenAI](https://youtu.be/pK6WzlTOlYw?si=fkcDQsBG2h-DM8uQ) by [Akamai Developer
](https://www.youtube.com/@AkamaiDeveloper)
- ⛓ [Retrieval-Augmented Generation (RAG) using LangChain and `Pinecone` - The RAG Special Episode](https://youtu.be/J_tCD_J6w3s?si=60Mnr5VD9UED9bGG) by [Generative AI and Data Science On AWS](https://www.youtube.com/@GenerativeAIOnAWS)
- ⛓ [`LLAMA2 70b-chat` Multiple Documents Chatbot with Langchain & Streamlit |All OPEN SOURCE|Replicate API](https://youtu.be/vhghB81vViM?si=dszzJnArMeac7lyc) by [DataInsightEdge](https://www.youtube.com/@DataInsightEdge01)
- ⛓ [Chatting with 44K Fashion Products: LangChain Opportunities and Pitfalls](https://youtu.be/Zudgske0F_s?si=8HSshHoEhh0PemJA) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
- ⛓ [Structured Data Extraction from `ChatGPT` with LangChain](https://youtu.be/q1lYg8JISpQ?si=0HctzOHYZvq62sve) by [MG](https://www.youtube.com/@MG_cafe)
- ⛓ [Chat with Multiple PDFs using `Llama 2`, `Pinecone` and LangChain (Free LLMs and Embeddings)](https://youtu.be/TcJ_tVSGS4g?si=FZYnMDJyoFfL3Z2i) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal)
- ⛓ [Integrate Audio into `LangChain.js` apps in 5 Minutes](https://youtu.be/hNpUSaYZIzs?si=Gb9h7W9A8lzfvFKi) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
- ⛓ [`ChatGPT` for your data with Local LLM](https://youtu.be/bWrjpwhHEMU?si=uM6ZZ18z9og4M90u) by [Jacob Jedryszek](https://www.youtube.com/@jj09)
- ⛓ [Training `Chatgpt` with your personal data using langchain step by step in detail](https://youtu.be/j3xOMde2v9Y?si=179HsiMU-hEPuSs4) by [NextGen Machines](https://www.youtube.com/@MayankGupta-kb5yc)
- ⛓ [Use ANY language in `LangSmith` with REST](https://youtu.be/7BL0GEdMmgY?si=iXfOEdBLqXF6hqRM) by [Nerding I/O](https://www.youtube.com/@nerding_io)
- ⛓ [How to Leverage the Full Potential of LLMs for Your Business with Langchain - Leon Ruddat](https://youtu.be/vZmoEa7oWMg?si=ZhMmydq7RtkZd56Q) by [PyData](https://www.youtube.com/@PyDataTV)
- ⛓ [`ChatCSV` App: Chat with CSV files using LangChain and `Llama 2`](https://youtu.be/PvsMg6jFs8E?si=Qzg5u5gijxj933Ya) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal)
- ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
- ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
### [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)
- [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
- [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
- [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
Only videos with 40K+ views:
- [Using `ChatGPT` with YOUR OWN Data. This is magical. (LangChain `OpenAI API`)](https://youtu.be/9AXP7tCI9PI)
- [Chat with Multiple `PDFs` | LangChain App Tutorial in Python (Free LLMs and Embeddings)](https://youtu.be/dXxQ0LR-3Hg?si=pjXKhsHRzn10vOqX)
- [`Hugging Face` + Langchain in 5 mins | Access 200k+ FREE AI models for your AI apps](https://youtu.be/_j7JEDWuqLE?si=psimQscN3qo2dOa9)
- [LangChain Crash Course For Beginners | LangChain Tutorial](https://youtu.be/nAmC7SoVLd8?si=qJdvyG5-rnjqfdj1)
- [Vector Embeddings Tutorial – Code Your Own AI Assistant with GPT-4 API + LangChain + NLP](https://youtu.be/yfHHvmaMkcA?si=UBP3yw50cLm3a2nj)
- [Development with Large Language Models Tutorial – `OpenAI`, Langchain, Agents, `Chroma`](https://youtu.be/xZDB1naRUlk?si=v8J1q6oFHRyTkf7Y)
- [Langchain: `PDF` Chat App (GUI) | ChatGPT for Your PDF FILES | Step-by-Step Tutorial](https://youtu.be/RIWbalZ7sTo?si=LbKsCcuyv0BtnrTY)
- [Vector Search `RAG` Tutorial – Combine Your Data with LLMs with Advanced Search](https://youtu.be/JEBDfGqrAUA?si=pD7oxpfwWeJCxfBt)
- [LangChain Crash Course for Beginners](https://youtu.be/lG7Uxts9SXs?si=Yte4S5afN7KNCw0F)
- [Learn `RAG` From Scratch – Python AI Tutorial from a LangChain Engineer](https://youtu.be/sVcwVQRHIc8?si=_LN4g0vOgSdtlB3S)
- [`Llama 2` in LangChain — FIRST Open Source Conversational Agent!](https://youtu.be/6iHVJyX2e50?si=rtq1maPrzWKHbwVV)
- [LangChain Tutorial for Beginners | Generative AI Series](https://youtu.be/cQUUkZnyoD0?si=KYz-bvcocdqGh9f_)
- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=yS7T98VLfcWdkDek)
- [LangChain Explained In 15 Minutes - A MUST Learn For Python Programmers](https://youtu.be/mrjq3lFz23s?si=wkQGcSKUJjuiiEPf)
- [LLM Project | End to End LLM Project Using Langchain, `OpenAI` in Finance Domain](https://youtu.be/MoqgmWV1fm8?si=oVl-5kJVgd3a07Y_)
- [What is LangChain?](https://youtu.be/1bUy-1hGZpI?si=NZ0D51VM5y-DhjGe)
- [`RAG` + Langchain Python Project: Easy AI/Chat For Your Doc](https://youtu.be/tcqEUSNCn8I?si=RLcWPBVLIErRqdmU)
- [Getting Started With LangChain In 20 Minutes- Build Celebrity Search Application](https://youtu.be/_FpT1cwcSLg?si=X9qVazlXYucN_JBP)
- [LangChain GEN AI Tutorial – 6 End-to-End Projects using OpenAI, Google `Gemini Pro`, `LLAMA2`](https://youtu.be/x0AnCE9SE4A?si=_92gJYm7kb-V2bi0)
- [Complete Langchain GEN AI Crash Course With 6 End To End LLM Projects With OPENAI, `LLAMA2`, `Gemini Pro`](https://youtu.be/aWKrL4z5H6w?si=NVLi7Yiq0ccE7xXE)
- [AI Leader Reveals The Future of AI AGENTS (LangChain CEO)](https://youtu.be/9ZhbA0FHZYc?si=1r4P6kRvKVvEhRgE)
- [Learn How To Query Pdf using Langchain Open AI in 5 min](https://youtu.be/5Ghv-F1wF_0?si=ZZRjrWfeiFOVrcvu)
- [Reliable, fully local RAG agents with `LLaMA3`](https://youtu.be/-ROS6gfYIts?si=75CXA8W_BbnkIxcV)
- [Learn `LangChain.js` - Build LLM apps with JavaScript and `OpenAI`](https://youtu.be/HSZ_uaif57o?si=Icj-RAhwMT-vHaYA)
- [LLM Project | End to End LLM Project Using LangChain, Google Palm In Ed-Tech Industry](https://youtu.be/AjQPRomyd-k?si=eC3NT6kn02Lhpz-_)
- [Chatbot Answering from Your Own Knowledge Base: Langchain, `ChatGPT`, `Pinecone`, and `Streamlit`: | Code](https://youtu.be/nAKhxQ3hcMA?si=9Zd_Nd_jiYhtml5w)
- [LangChain is AMAZING | Quick Python Tutorial](https://youtu.be/I4mFqyqFkxg?si=aJ66qh558OfNAczD)
- [`GirlfriendGPT` - AI girlfriend with LangChain](https://youtu.be/LiN3D1QZGQw?si=kZR-lnJwixeVrjmh)
- [Using NEW `MPT-7B` in `Hugging Face` and LangChain](https://youtu.be/DXpk9K7DgMo?si=99JDpV_ueimwJhMi)
- [LangChain - COMPLETE TUTORIAL - Basics to advanced concept!](https://youtu.be/a89vqgK-Qcs?si=0aVO2EOqsw7GE5e3)
- [Chat With Multiple `PDF` Documents With Langchain And Google `Gemini Pro`](https://youtu.be/uus5eLz6smA?si=YUwvHtaZsGeIl0WD)
- [LLM Project | End to end LLM project Using Langchain, `Google Palm` in Retail Industry](https://youtu.be/4wtrl4hnPT8?si=_eOKPpdLfWu5UXMQ)
- [Tutorial | Chat with any Website using Python and Langchain](https://youtu.be/bupx08ZgSFg?si=KRrjYZFnuLsstGwW)
- [Prompt Engineering And LLM's With LangChain In One Shot-Generative AI](https://youtu.be/t2bSApmPzU4?si=87vPQQtYEWTyu2Kx)
- [Build a Custom Chatbot with `OpenAI`: `GPT-Index` & LangChain | Step-by-Step Tutorial](https://youtu.be/FIDv6nc4CgU?si=gR1u3DUG9lvzBIKK)
- [Search Your `PDF` App using Langchain, `ChromaDB`, and Open Source LLM: No OpenAI API (Runs on CPU)](https://youtu.be/rIV1EseKwU4?si=UxZEoXSiPai8fXgl)
- [Building a `RAG` application from scratch using Python, LangChain, and the `OpenAI API`](https://youtu.be/BrsocJb-fAo?si=hvkh9iTGzJ-LnsX-)
- [Function Calling via `ChatGPT API` - First Look With LangChain](https://youtu.be/0-zlUy7VUjg?si=Vc6LFseckEc6qvuk)
- [Private GPT, free deployment! Langchain-Chachat helps you easily play with major mainstream AI models! | Zero Degree Commentary](https://youtu.be/3LLUyaHP-3I?si=AZumEeFXsvqaLl0f)
- [Create a ChatGPT clone using `Streamlit` and LangChain](https://youtu.be/IaTiyQ2oYUQ?si=WbgsYmqPDnMidSUK)
- [What's next for AI agents ft. LangChain's Harrison Chase](https://youtu.be/pBBe1pk8hf4?si=H4vdBF9nmkNZxiHt)
- [`LangFlow`: Build Chatbots without Writing Code - LangChain](https://youtu.be/KJ-ux3hre4s?si=TJuDu4bAlva1myNL)
- [Building a LangChain Custom Medical Agent with Memory](https://youtu.be/6UFtRwWnHws?si=wymYad26VgigRkHy)
As of release 0.2.0, `langchain` is required to be integration-agnostic. This means that code in `langchain` should not by default instantiate any specific chat models, llms, embedding models, vectorstores etc; instead, the user will be required to specify those explicitly.
The following functions and classes require an explicit LLM to be passed as an argument:
The following classes now require passing an explicit Embedding model as an argument:
- `langchain.indexes.VectostoreIndexCreator`
The following code has been removed:
- `langchain.natbot.NatBotChain.from_default` removed in favor of the `from_llm` class method.
### Deprecated
We have two main types of deprecations:
1. Code that was moved from `langchain` into another package (e.g, `langchain-community`)
If you try to import it from `langchain`, the import will keep on working, but will raise a deprecation warning. The warning will provide a replacement import statement.
LangChainDeprecationWarning: Importing UnstructuredMarkdownLoader from langchain.document_loaders is deprecated. Please replace deprecated imports:
>> from langchain.document_loaders import UnstructuredMarkdownLoader
with new imports of:
>> from langchain_community.document_loaders import UnstructuredMarkdownLoader
```
We will continue supporting the imports in `langchain` until release 0.4 as long as the relevant package where the code lives is installed. (e.g., as long as `langchain_community` is installed.)
However, we advise for users to not rely on these imports and instead migrate to the new imports. To help with this process, we’re releasing a migration script via the LangChain CLI. See further instructions in migration guide.
1. Code that has better alternatives available and will eventually be removed, so there’s only a single way to do things. (e.g., `predict_messages` method in ChatModels has been deprecated in favor of `invoke`).
Many of these were marked for removal in 0.2. We have bumped the removal to 0.3.
## 0.1.0 (Jan 5, 2024)
#### Deleted
### Deleted
No deletions.
#### Deprecated
### Deprecated
Deprecated classes and methods will be removed in 0.2.0
Here are some things to keep in mind for all types of contributions:
- Follow the ["fork and pull request"](https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project) workflow.
- Fill out the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers.
- Ensure your PR passes formatting, linting, and testing checks before requesting a review.
- If you would like comments or feedback on your current progress, please open an issue or discussion and tag a maintainer.
- See the sections on [Testing](/docs/contributing/code/setup#testing) and [Formatting and Linting](/docs/contributing/code/setup#formatting-and-linting) for how to run these checks locally.
- Backwards compatibility is key. Your changes must not be breaking, except in case of critical bug and security fixes.
- Look for duplicate PRs or issues that have already been opened before opening a new one.
- Keep scope as isolated as possible. As a general rule, your changes should not affect more than one package at a time.
## Bugfixes
We encourage and appreciate bugfixes. We ask that you:
- Explain the bug in enough detail for maintainers to be able to reproduce it.
- If an accompanying issue exists, link to it. Prefix with `Fixes` so that the issue will close automatically when the PR is merged.
- Avoid breaking changes if possible.
- Include unit tests that fail without the bugfix.
If you come across a bug and don't know how to fix it, we ask that you open an issue for it describing in detail the environment in which you encountered the bug.
## New features
We aim to keep the bar high for new features. We generally don't accept new core abstractions, changes to infra, changes to dependencies,
or new agents/chains from outside contributors without an existing GitHub discussion or issue that demonstrates an acute need for them.
- New features must come with docs, unit tests, and (if appropriate) integration tests.
- New integrations must come with docs, unit tests, and (if appropriate) integration tests.
- See [this page](/docs/contributing/integrations) for more details on contributing new integrations.
- New functionality should not inherit from or use deprecated methods or classes.
- We will reject features that are likely to lead to security vulnerabilities or reports.
- Do not add any hard dependencies. Integrations may add optional dependencies.
To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
Please do not try to push directly to this repo unless you are a maintainer.
Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
maintainers.
Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
[Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
It's essential that we maintain great documentation and testing. If you:
- Fix a bug
- Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
- Make an improvement
- Update any affected example notebooks and documentation. These live in `docs`.
- Update unit and integration tests when relevant.
- Add a feature
- Add a demo notebook in `docs/docs/`.
- Add unit and integration tests.
We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
best way to get our attention.
## 🚀 Quick Start
This quick start guide explains how to run the repository locally.
This guide walks through how to run the repository locally and check in your first code.
For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer).
### Dependency Management: Poetry and other env/dependency managers
## Dependency Management: Poetry and other env/dependency managers
This project utilizes [Poetry](https://python-poetry.org/) v1.7.1+ as a dependency manager.
@@ -41,7 +14,7 @@ Install Poetry: **[documentation on how to install it](https://python-poetry.org
❗Note: If you use `Conda` or `Pyenv` as your environment/package manager, after installing Poetry,
tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
### Different packages
## Different packages
This repository contains multiple packages:
- `langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
@@ -59,7 +32,7 @@ For this quickstart, start with langchain-community:
cd libs/community
```
### Local Development Dependencies
## Local Development Dependencies
Install langchain-community development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
@@ -79,9 +52,9 @@ If you are still seeing this bug on v1.6.1+, you may also try disabling "modern
(`poetry config installer.modern-installation false`) and re-installing requirements.
See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
### Testing
## Testing
_In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional; see section about optional dependencies_.
**Note:** In `langchain`, `langchain-community`, and `langchain-experimental`, some test dependencies are optional. See the following section about optional dependencies.
Unit tests cover modular logic that does not require calls to outside APIs.
If you add new logic, please add a unit test.
@@ -118,11 +91,11 @@ poetry install --with test
make test
```
### Formatting and Linting
## Formatting and Linting
Run these locally before submitting a PR; the CI system will check also.
#### Code Formatting
### Code Formatting
Formatting for this project is done via [ruff](https://docs.astral.sh/ruff/rules/).
@@ -174,7 +147,7 @@ This can be very helpful when you've made changes to only certain parts of the p
We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
#### Spellcheck
### Spellcheck
Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
LangChain documentation consists of two components:
@@ -12,8 +16,6 @@ used to generate the externally facing [API Reference](https://api.python.langch
The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
developers document their code well.
The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
The `API Reference` is largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/)
from the code and is hosted by [Read the Docs](https://readthedocs.org/).
@@ -29,7 +31,7 @@ The content for the main documentation is located in the `/docs` directory of th
The documentation is written using a combination of ipython notebooks (`.ipynb` files)
and markdown (`.mdx` files). The notebooks are converted to markdown
using [Quarto](https://quarto.org) and then built using [Docusaurus 2](https://docusaurus.io/).
and then built using [Docusaurus 2](https://docusaurus.io/).
Feel free to make contributions to the main documentation! 🥰
@@ -48,10 +50,6 @@ locally to ensure that it looks good and is free of errors.
If you're unable to build it locally that's okay as well, as you will be able to
see a preview of the documentation on the pull request page.
### Install dependencies
- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus. [Download link](https://quarto.org/docs/download/).
From the **monorepo root**, run the following command to install the dependencies:
```bash
@@ -78,6 +76,18 @@ make docs_build
make api_docs_build
```
:::tip
The `make api_docs_build` command takes a long time. If you're making cosmetic changes to the API docs and want to see how they look, use:
```bash
make api_docs_quick_preview
```
which will just build a small subset of the API reference.
:::
Finally, run the link checker to ensure all links are valid:
[References](/docs/contributing/documentation/style_guide/#references), and [Explanations](/docs/contributing/documentation/style_guide/#conceptual-guide).
- **Tutorials**: Lessons that take the reader by the hand through a series of conceptual steps to complete a project.
- An example of this is our [LCEL streaming guide](/docs/expression_language/streaming).
- Our guides on [custom components](/docs/modules/model_io/chat/custom_chat_model) is another one.
- **How-to guides**: Guides that take the reader through the steps required to solve a real-world problem.
- The clearest examples of this are our [Use case](/docs/use_cases/) quickstart pages.
- **Reference**: Technical descriptions of the machinery and how to operate it.
- Our [Runnable interface](/docs/expression_language/interface) page is an example of this.
- The [API reference pages](https://api.python.langchain.com/) are another.
- **Explanation**: Explanations that clarify and illuminate a particular topic.
- The [LCEL primitives pages](/docs/expression_language/primitives/sequence) are an example of this.
### Tutorials
Tutorials are lessons that take the reader through a practical activity. Their purpose is to help the user
gain understanding of concepts and how they interact by showing one way to achieve some goal in a hands-on way. They should **avoid** giving
multiple permutations of ways to achieve that goal in-depth. Instead, it should guide a new user through a recommended path to accomplishing the tutorial's goal. While the end result of a tutorial does not necessarily need to
be completely production-ready, it should be useful and practically satisfy the the goal that you clearly stated in the tutorial's introduction. Information on how to address additional scenarios
belongs in how-to guides.
To quote the Diataxis website:
> A tutorial serves the user’s*acquisition*of skills and knowledge - their study. Its purpose is not to help the user get something done, but to help them learn.
In LangChain, these are often higher level guides that show off end-to-end use cases.
Some examples include:
- [Build a Simple LLM Application with LCEL](/docs/tutorials/llm_chain/)
- [Build a Retrieval Augmented Generation (RAG) App](/docs/tutorials/rag/)
Here are some high-level tips on writing a good tutorial:
- Focus on guiding the user to get something done, but keep in mind the end-goal is more to impart principles than to create a perfect production system.
- Be specific, not abstract and follow one path.
- No need to go deeply into alternative approaches, but it’s ok to reference them, ideally with a link to an appropriate how-to guide.
- Get "a point on the board" as soon as possible - something the user can run that outputs something.
- You can iterate and expand afterwards.
- Try to frequently checkpoint at given steps where the user can run code and see progress.
- Focus on results, not technical explanation.
- Crosslink heavily to appropriate conceptual/reference pages.
- The first time you mention a LangChain concept, use its full name (e.g. "LangChain Expression Language (LCEL)"), and link to its conceptual/other documentation page.
- It's also helpful to add a prerequisite callout that links to any pages with necessary background information.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as related how-to guides.
### How-to guides
A how-to guide, as the name implies, demonstrates how to do something discrete and specific.
It should assume that the user is already familiar with underlying concepts, and is trying to solve an immediate problem, but
should still give some background or list the scenarios where the information contained within can be relevant.
They can and should discuss alternatives if one approach may be better than another in certain cases.
To quote the Diataxis website:
> A how-to guide serves the work of the already-competent user, whom you can assume to know what they want to do, and to be able to follow your instructions correctly.
Some examples include:
- [How to: return structured data from a model](/docs/how_to/structured_output/)
- [How to: write a custom chat model](/docs/how_to/custom_chat_model/)
Here are some high-level tips on writing a good how-to guide:
- Clearly explain what you are guiding the user through at the start.
- Assume higher intent than a tutorial and show what the user needs to do to get that task done.
- Assume familiarity of concepts, but explain why suggested actions are helpful.
- Crosslink heavily to conceptual/reference pages.
- Discuss alternatives and responses to real-world tradeoffs that may arise when solving a problem.
- Use lots of example code.
- Prefer full code blocks that the reader can copy and run.
- End with a recap/next steps section summarizing what the tutorial covered and future reading, such as other related how-to guides.
### Conceptual guide
LangChain's conceptual guide falls under the **Explanation** quadrant of Diataxis. They should cover LangChain terms and concepts
in a more abstract way than how-to guides or tutorials, and should be geared towards curious users interested in
gaining a deeper understanding of the framework. Try to avoid excessively large code examples - the goal here is to
impart perspective to the user rather than to finish a practical project. These guides should cover **why** things work they way they do.
This guide on documentation style is meant to fall under this category.
To quote the Diataxis website:
> The perspective of explanation is higher and wider than that of the other types. It does not take the user’s eye-level view, as in a how-to guide, or a close-up view of the machinery, like reference material. Its scope in each case is a topic - “an area of knowledge”, that somehow has to be bounded in a reasonable, meaningful way.
- [Chat model conceptual docs](/docs/concepts/#chat-models)
Here are some high-level tips on writing a good conceptual guide:
- Explain design decisions. Why does concept X exist and why was it designed this way?
- Use analogies and reference other concepts and alternatives
- Avoid blending in too much reference content
- You can and should reference content covered in other guides, but make sure to link to them
### References
References contain detailed, low-level information that describes exactly what functionality exists and how to use it.
In LangChain, this is mainly our API reference pages, which are populated from docstrings within code.
References pages are generally not read end-to-end, but are consulted as necessary when a user needs to know
how to use something specific.
To quote the Diataxis website:
> The only purpose of a reference guide is to describe, as succinctly as possible, and in an orderly way. Whereas the content of tutorials and how-to guides are led by needs of the user, reference material is led by the product it describes.
Many of the reference pages in LangChain are automatically generated from code,
but here are some high-level tips on writing a good docstring:
- Be concise
- Discuss special cases and deviations from a user's expectations
- Go into detail on required inputs and outputs
- Light details on when one might use the feature are fine, but in-depth details belong in other sections.
Each category serves a distinct purpose and requires a specific approach to writing and structuring the content.
## Taxonomy
Keeping the above in mind, we have sorted LangChain's docs into categories. It is helpful to think in these terms
when contributing new documentation:
### Getting started
The [getting started section](/docs/get_started/introduction) includes a high-level introduction to LangChain, a quickstart that
tours LangChain's various features, and logistical instructions around installation and project setup.
It contains elements of **How-to guides** and **Explanations**.
### Use cases
[Use cases](/docs/use_cases/) are guides that are meant to show how to use LangChain to accomplish a specific task (RAG, information extraction, etc.).
The quickstarts should be good entrypoints for first-time LangChain developers who prefer to learn by getting something practical prototyped,
then taking the pieces apart retrospectively. These should mirror what LangChain is good at.
The quickstart pages here should fit the **How-to guide** category, with the other pages intended to be **Explanations** of more
in-depth concepts and strategies that accompany the main happy paths.
:::note
The below sections are listed roughly in order of increasing level of abstraction.
:::
### Expression Language
[LangChain Expression Language (LCEL)](/docs/expression_language/) is the fundamental way that most LangChain components fit together, and this section is designed to teach
developers how to use it to build with LangChain's primitives effectively.
This section should contains **Tutorials** that teach how to stream and use LCEL primitives for more abstract tasks, **Explanations** of specific behaviors,
and some **References** for how to use different methods in the Runnable interface.
### Components
The [components section](/docs/modules) covers concepts one level of abstraction higher than LCEL.
Abstract base classes like `BaseChatModel` and `BaseRetriever` should be covered here, as well as core implementations of these base classes,
such as `ChatPromptTemplate` and `RecursiveCharacterTextSplitter`. Customization guides belong here too.
This section should contain mostly conceptual **Tutorials**, **References**, and **Explanations** of the components they cover.
:::note
As a general rule of thumb, everything covered in the `Expression Language` and `Components` sections (with the exception of the `Composition` section of components) should
cover only components that exist in `langchain_core`.
:::
### Integrations
The [integrations](/docs/integrations/platforms/) are specific implementations of components. These often involve third-party APIs and services.
If this is the case, as a general rule, these are maintained by the third-party partner.
This section should contain mostly **Explanations** and **References**, though the actual content here is more flexible than other sections and more at the
discretion of the third-party provider.
:::note
Concepts covered in `Integrations` should generally exist in `langchain_community` or specific partner packages.
:::
### Guides and Ecosystem
The [Guides](/docs/guides) and [Ecosystem](/docs/langsmith/) sections should contain guides that address higher-level problems than the sections above.
This includes, but is not limited to, considerations around productionization and development workflows.
These should contain mostly **How-to guides**, **Explanations**, and **Tutorials**.
### API references
LangChain's API references. Should act as **References** (as the name implies) with some **Explanation**-focused content as well.
## Sample developer journey
We have set up our docs to assist a new developer to LangChain. Let's walk through the intended path:
- The developer lands on https://python.langchain.com, and reads through the introduction and the diagram.
- If they are just curious, they may be drawn to the [Quickstart](/docs/get_started/quickstart) to get a high-level tour of what LangChain contains.
- If they have a specific task in mind that they want to accomplish, they will be drawn to the Use-Case section. The use-case should provide a good, concrete hook that shows the value LangChain can provide them and be a good entrypoint to the framework.
- They can then move to learn more about the fundamentals of LangChain through the Expression Language sections.
- Next, they can learn about LangChain's various components and integrations.
- Finally, they can get additional knowledge through the Guides.
This is only an ideal of course - sections will inevitably reference lower or higher-level concepts that are documented in other sections.
## Guidelines
## General guidelines
Here are some other guidelines you should think about when writing and organizing documentation.
### Linking to other sections
We generally do not merge new tutorials from outside contributors without an actue need.
We welcome updates as well as new integration docs, how-tos, and references.
### Avoid duplication
Multiple pages that cover the same material in depth are difficult to maintain and cause confusion. There should
be only one (very rarely two), canonical pages for a given concept or feature. Instead, you should link to other guides.
### Link to other sections
Because sections of the docs do not exist in a vacuum, it is important to link to other sections as often as possible
to allow a developer to learn more about an unfamiliar topic inline.
This includes linking to the API references as well as conceptual sections!
### Conciseness
### Be concise
In general, take a less-is-more approach. If a section with a good explanation of a concept already exists, you should link to it rather than
re-explain it, unless the concept you are documenting presents some new wrinkle.
@@ -130,9 +149,10 @@ Be concise, including in code samples.
### General style
- Use active voice and present tense whenever possible.
- Use examples and code snippets to illustrate concepts and usage.
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically.
- Use bullet points and numbered lists to break down information into easily digestible chunks.
- Use tables (especially for **Reference** sections) and diagrams often to present information visually.
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages.
- Use active voice and present tense whenever possible
- Use examples and code snippets to illustrate concepts and usage
- Use appropriate header levels (`#`, `##`, `###`, etc.) to organize the content hierarchically
- Use fewer cells with more code to make copy/paste easier
- Use bullet points and numbered lists to break down information into easily digestible chunks
- Use tables (especially for **Reference** sections) and diagrams often to present information visually
- Include the table of contents for longer documentation pages to help readers navigate the content, but hide it for shorter pages
"'El país donde se encuentra la ciudad de Honolulu, donde nació Barack Obama, el 44º Presidente de los Estados Unidos, es Estados Unidos. Honolulu se encuentra en la isla de Oahu, en el estado de Hawái.'"
"ChatPromptValue(messages=[HumanMessage(content='What is the color of strawberry and the flag of China?', additional_kwargs={}, example=False)])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"question_generator.invoke(\"warm\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b4a9812b-bead-4fd9-ae27-0b8be57e5dc1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='The color of an apple is typically red or green. The flag of China is predominantly red with a large yellow star in the upper left corner and four smaller yellow stars surrounding it.', additional_kwargs={}, example=False)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prompt = question_generator.invoke(\"warm\")\n",
"model.invoke(prompt)"
]
},
{
"cell_type": "markdown",
"id": "6d75a313-f1c8-4e94-9a17-24e0bf4a2bdc",
"metadata": {},
"source": [
"### Branching and Merging\n",
"\n",
"You may want the output of one component to be processed by 2 or more other components. [RunnableParallels](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableParallel.html#langchain_core.runnables.base.RunnableParallel) let you split or fork the chain so multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:\n",
"\n",
"```text\n",
" Input\n",
" / \\\n",
" / \\\n",
" Branch1 Branch2\n",
" \\ /\n",
" \\ /\n",
" Combine\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "247fa0bd-4596-4063-8cb3-1d7fc119d982",
"metadata": {},
"outputs": [],
"source": [
"planner = (\n",
" ChatPromptTemplate.from_template(\"Generate an argument about: {input}\")\n",
"'While Scrum has its potential cons and challenges, many organizations have successfully embraced and implemented this project management framework to great effect. The cons mentioned above can be mitigated or overcome with proper training, support, and a commitment to continuous improvement. It is also important to note that not all cons may be applicable to every organization or project.\\n\\nFor example, while Scrum may be complex initially, with proper training and guidance, teams can quickly grasp the concepts and practices. The lack of predictability can be mitigated by implementing techniques such as velocity tracking and release planning. The limited documentation can be addressed by maintaining a balance between lightweight documentation and clear communication among team members. The dependency on team collaboration can be improved through effective communication channels and regular team-building activities.\\n\\nScrum can be scaled and adapted to larger projects by using frameworks like Scrum of Scrums or LeSS (Large Scale Scrum). Concerns about speed versus quality can be addressed by incorporating quality assurance practices, such as continuous integration and automated testing, into the Scrum process. Scope creep can be managed by having a well-defined and prioritized product backlog, and a strong product owner can be developed through training and mentorship.\\n\\nResistance to change can be overcome by providing proper education and communication to stakeholders and involving them in the decision-making process. Ultimately, the cons of Scrum can be seen as opportunities for growth and improvement, and with the right mindset and support, they can be effectively managed.\\n\\nIn conclusion, while Scrum may have its challenges and potential cons, the benefits and advantages it offers in terms of collaboration, flexibility, adaptability, transparency, and customer satisfaction make it a widely adopted and successful project management framework. With proper implementation and continuous improvement, organizations can leverage Scrum to drive innovation, efficiency, and project success.'"
"Almost any other chains you build will use this building block."
]
},
{
"cell_type": "markdown",
"id": "93aa2c87",
"metadata": {},
"source": [
"## PromptTemplate + LLM\n",
"\n",
"The simplest composition is just combining a prompt and model to create a chain that takes user input, adds it to a prompt, passes it to a model, and returns the raw model output.\n",
"\n",
"Note, you can mix and match PromptTemplate/ChatPromptTemplates and LLMs/ChatModels as you like here."
"LCEL makes it easy to build complex chains from basic components, and supports out of the box functionality such as streaming, parallelism, and logging."
]
},
{
"cell_type": "markdown",
"id": "9a9acd2e",
"metadata": {},
"source": [
"## Basic example: prompt + model + output parser\n",
"\n",
"The most basic and common use case is chaining a prompt template and a model together. To see how this works, let's create a chain that takes a topic and generates a joke:"
"prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
"output_parser = StrOutputParser()\n",
"\n",
"chain = prompt | model | output_parser\n",
"\n",
"chain.invoke({\"topic\": \"ice cream\"})"
]
},
{
"cell_type": "markdown",
"id": "81c502c5-85ee-4f36-aaf4-d6e350b7792f",
"metadata": {},
"source": [
"Notice this line of the code, where we piece together these different components into a single chain using LCEL:\n",
"\n",
"```\n",
"chain = prompt | model | output_parser\n",
"```\n",
"\n",
"The `|` symbol is similar to a [unix pipe operator](https://en.wikipedia.org/wiki/Pipeline_(Unix)), which chains together the different components, feeding the output from one component as input into the next component. \n",
"\n",
"In this chain the user input is passed to the prompt template, then the prompt template output is passed to the model, then the model output is passed to the output parser. Let's take a look at each component individually to really understand what's going on."
]
},
{
"cell_type": "markdown",
"id": "aa1b77fa",
"metadata": {},
"source": [
"### 1. Prompt\n",
"\n",
"`prompt` is a `BasePromptTemplate`, which means it takes in a dictionary of template variables and produces a `PromptValue`. A `PromptValue` is a wrapper around a completed prompt that can be passed to either an `LLM` (which takes a string as input) or `ChatModel` (which takes a sequence of messages as input). It can work with either language model type because it defines logic both for producing `BaseMessage`s and for producing a string."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b8656990",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])"
"And lastly we pass our `model` output to the `output_parser`, which is a `BaseOutputParser` meaning it takes either a string or a \n",
"`BaseMessage` as input. The specific `StrOutputParser` simply converts any input into a string."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "533e59a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"Why did the ice cream go to therapy? \\n\\nBecause it had too many toppings and couldn't find its cone-fidence!\""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_parser.invoke(message)"
]
},
{
"cell_type": "markdown",
"id": "9851e842",
"metadata": {},
"source": [
"### 4. Entire Pipeline\n",
"\n",
"To follow the steps along:\n",
"\n",
"1. We pass in user input on the desired topic as `{\"topic\": \"ice cream\"}`\n",
"2. The `prompt` component takes the user input, which is then used to construct a PromptValue after using the `topic` to construct the prompt. \n",
"3. The `model` component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a `ChatMessage` object. \n",
"4. Finally, the `output_parser` component takes in a `ChatMessage`, and transforms this into a Python string, which is returned from the invoke method. \n"
"Note that if you’re curious about the output of any components, you can always test out a smaller version of the chain such as `prompt` or `prompt | model` to see the intermediate results:\n",
"\n",
":::"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11089b6f-23f8-474f-97ec-8cae8d0ca6d4",
"metadata": {},
"outputs": [],
"source": [
"input = {\"topic\": \"ice cream\"}\n",
"\n",
"prompt.invoke(input)\n",
"# > ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])\n",
"\n",
"(prompt | model).invoke(input)\n",
"# > AIMessage(content=\"Why did the ice cream go to therapy?\\nBecause it had too many toppings and couldn't cone-trol itself!\")"
]
},
{
"cell_type": "markdown",
"id": "cc7d3b9d-e400-4c9b-9188-f29dac73e6bb",
"metadata": {},
"source": [
"## RAG Search Example\n",
"\n",
"For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions."
"chain = setup_and_retrieval | prompt | model | output_parser\n",
"\n",
"chain.invoke(\"where did harrison work?\")"
]
},
{
"cell_type": "markdown",
"id": "f0999140-6001-423b-970b-adf1dfdb4dec",
"metadata": {},
"source": [
"In this case, the composed chain is: "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b88e9bb-f04a-4a56-87ec-19a0e6350763",
"metadata": {},
"outputs": [],
"source": [
"chain = setup_and_retrieval | prompt | model | output_parser"
]
},
{
"cell_type": "markdown",
"id": "6e929e15-40a5-4569-8969-384f636cab87",
"metadata": {},
"source": [
"To explain this, we first can see that the prompt template above takes in `context` and `question` as values to be substituted in the prompt. Before building the prompt template, we want to retrieve relevant documents to the search and include them as part of the context. \n",
"\n",
"As a preliminary step, we’ve setup the retriever using an in memory store, which can retrieve documents based on a query. This is a runnable component as well that can be chained together with other components, but you can also try to run it separately:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7319ef6-613b-4638-ad7d-4a2183702c1d",
"metadata": {},
"outputs": [],
"source": [
"retriever.invoke(\"where did harrison work?\")"
]
},
{
"cell_type": "markdown",
"id": "e6833844-f1c4-444c-a3d2-31b3c6b31d46",
"metadata": {},
"source": [
"We then use the `RunnableParallel` to prepare the expected inputs into the prompt by using the entries for the retrieved documents as well as the original user question, using the retriever for document search, and `RunnablePassthrough` to pass the user’s question:"
"chain = setup_and_retrieval | prompt | model | output_parser"
]
},
{
"cell_type": "markdown",
"id": "5c6f5f74-b387-48a0-bedd-1fae202cd10a",
"metadata": {},
"source": [
"With the flow being:\n",
"\n",
"1. The first steps create a `RunnableParallel` object with two entries. The first entry, `context` will include the document results fetched by the retriever. The second entry, `question` will contain the user’s original question. To pass on the question, we use `RunnablePassthrough` to copy this entry. \n",
"2. Feed the dictionary from the step above to the `prompt` component. It then takes the user input which is `question` as well as the retrieved document which is `context` to construct a prompt and output a PromptValue. \n",
"3. The `model` component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a `ChatMessage` object. \n",
"4. Finally, the `output_parser` component takes in a `ChatMessage`, and transforms this into a Python string, which is returned from the invoke method.\n",
"\n",
"```mermaid\n",
"graph LR\n",
" A(Question) --> B(RunnableParallel)\n",
" B -->|Question| C(Retriever)\n",
" B -->|Question| D(RunnablePassThrough)\n",
" C -->|context=retrieved docs| E(PromptTemplate)\n",
" D -->|question=Question| E\n",
" E -->|PromptValue| F(ChatModel) \n",
" F -->|ChatMessage| G(StrOutputParser)\n",
" G --> |String| H(Result)\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "8c2438df-164e-4bbe-b5f4-461695e45b0f",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"We recommend reading our [Advantages of LCEL](/docs/expression_language/why) section next to see a side-by-side comparison of the code needed to produce common functionality with and without LCEL."
"# Create a runnable with the @chain decorator\n",
"\n",
"You can also turn an arbitrary function into a chain by adding a `@chain` decorator. This is functionaly equivalent to wrapping in a [`RunnableLambda`](/docs/expression_language/primitives/functions).\n",
"\n",
"This will have the benefit of improved observability by tracing your chain correctly. Any calls to runnables inside this function will be traced as nested childen.\n",
"\n",
"It will also allow you to use this as any other runnable, compose it in chain, etc.\n",
"The `RunnableWithMessageHistory` lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it.\n",
"\n",
"Specifically, it can be used for any Runnable that takes as input one of\n",
"\n",
"* a sequence of `BaseMessage`\n",
"* a dict with a key that takes a sequence of `BaseMessage`\n",
"* a dict with a key that takes the latest message(s) as a string or sequence of `BaseMessage`, and a separate key that takes historical messages\n",
"\n",
"And returns as output one of\n",
"\n",
"* a string that can be treated as the contents of an `AIMessage`\n",
"* a sequence of `BaseMessage`\n",
"* a dict with a key that contains a sequence of `BaseMessage`\n",
"\n",
"Let's take a look at some examples to see how it works. First we construct a runnable (which here accepts a dict as input and returns a message as output):"
"2. A callable that returns an instance of `BaseChatMessageHistory`.\n",
"\n",
"Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using Redis and other providers. Here we demonstrate using an in-memory `ChatMessageHistory` as well as more persistent storage using `RedisChatMessageHistory`."
]
},
{
"cell_type": "markdown",
"id": "3d83adad-9672-496d-9f25-5747e7b8c8bb",
"metadata": {},
"source": [
"## In-memory\n",
"\n",
"Below we show a simple example in which the chat history lives in memory, in this case via a global Python dict.\n",
"\n",
"We construct a callable `get_session_history` that references this dict to return an instance of `ChatMessageHistory`. The arguments to the callable can be specified by passing a configuration to the `RunnableWithMessageHistory` at runtime. By default, the configuration parameter is expected to be a single string `session_id`. This can be adjusted via the `history_factory_config` kwarg.\n",
"Note that we've specified `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to).\n",
"\n",
"When invoking this new runnable, we specify the corresponding chat history via a configuration parameter:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "01384412-f08e-4634-9edb-3f46f475b582",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Cosine is a trigonometric function that calculates the ratio of the adjacent side to the hypotenuse of a right triangle.')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with_message_history.invoke(\n",
" {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
"The configuration parameters by which we track message histories can be customized by passing in a list of ``ConfigurableFieldSpec`` objects to the ``history_factory_config`` parameter. Below, we use two parameters: a `user_id` and `conversation_id`."
"### Examples with runnables of different signatures\n",
"\n",
"The above runnable takes a dict as input and returns a BaseMessage. Below we show some alternatives."
]
},
{
"cell_type": "markdown",
"id": "48eae1bf-b59d-4a61-8e62-b6dbf667e866",
"metadata": {},
"source": [
"#### Messages input, dict output"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "17733d4f-3a32-4055-9d44-5d58b9446a26",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'output_message': AIMessage(content=\"Simone de Beauvoir believed in the existence of free will. She argued that individuals have the ability to make choices and determine their own actions, even in the face of social and cultural constraints. She rejected the idea that individuals are purely products of their environment or predetermined by biology or destiny. Instead, she emphasized the importance of personal responsibility and the need for individuals to actively engage in creating their own lives and defining their own existence. De Beauvoir believed that freedom and agency come from recognizing one's own freedom and actively exercising it in the pursuit of personal and collective liberation.\")}"
"{'output_message': AIMessage(content='Simone de Beauvoir\\'s views on free will were closely aligned with those of her contemporary and partner Jean-Paul Sartre. Both de Beauvoir and Sartre were existentialist philosophers who emphasized the importance of individual freedom and the rejection of determinism. They believed that human beings have the capacity to transcend their circumstances and create their own meaning and values.\\n\\nSartre, in his famous work \"Being and Nothingness,\" argued that human beings are condemned to be free, meaning that we are burdened with the responsibility of making choices and defining ourselves in a world that lacks inherent meaning. Like de Beauvoir, Sartre believed that individuals have the ability to exercise their freedom and make choices in the face of external and internal constraints.\\n\\nWhile there may be some nuanced differences in their philosophical writings, overall, de Beauvoir and Sartre shared a similar belief in the existence of free will and the importance of individual agency in shaping one\\'s own life.')}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with_message_history.invoke(\n",
" [HumanMessage(content=\"How did this compare to Sartre\")],\n",
"In many cases it is preferable to persist conversation histories. `RunnableWithMessageHistory` is agnostic as to how the `get_session_history` callable retrieves its chat message histories. See [here](https://github.com/langchain-ai/langserve/blob/main/examples/chat_with_persistence_and_user/server.py) for an example using a local filesystem. Below we demonstrate how one could use Redis. Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using other providers."
]
},
{
"cell_type": "markdown",
"id": "6bca45e5-35d9-4603-9ca9-6ac0ce0e35cd",
"metadata": {},
"source": [
"### Setup\n",
"\n",
"We'll need to install Redis if it's not installed already:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "477d04b3-c2b6-4ba5-962f-492c0d625cd5",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet redis"
]
},
{
"cell_type": "markdown",
"id": "6a0ec9e0-7b1c-4c6f-b570-e61d520b47c6",
"metadata": {},
"source": [
"Start a local Redis Stack server if we don't have an existing Redis deployment to connect to:\n",
"```bash\n",
"docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cd6a250e-17fe-4368-a39d-1fe6b2cbde68",
"metadata": {},
"outputs": [],
"source": [
"REDIS_URL = \"redis://localhost:6379/0\""
]
},
{
"cell_type": "markdown",
"id": "36f43b87-655c-4f64-aa7b-bd8c1955d8e5",
"metadata": {},
"source": [
"### [LangSmith](/docs/langsmith)\n",
"\n",
"LangSmith is especially useful for something like message history injection, where it can be hard to otherwise understand what the inputs are to various parts of the chain.\n",
"\n",
"Note that LangSmith is not needed, but it is helpful.\n",
"If you do want to use LangSmith, after you sign up at the link above, make sure to uncoment the below and set your environment variables to start logging traces:"
"AIMessage(content='The inverse of cosine is the arccosine function, denoted as acos or cos^-1, which gives the angle corresponding to a given cosine value.')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with_message_history.invoke(\n",
" {\"ability\": \"math\", \"input\": \"What's its inverse\"},\n",
"Looking at the Langsmith trace for the second call, we can see that when constructing the prompt, a \"history\" variable has been injected which is a list of two messages (our first input and first output)."
LangChain Expression Language, or LCEL, is a declarative way to easily compose chains together.
LCEL was designed from day 1 to **support putting prototypes in production, with no code changes**, from the simplest “prompt + LLM” chain to the most complex chains (we’ve seen folks successfully run LCEL chains with 100s of steps in production). To highlight a few of the reasons you might want to use LCEL:
When you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens.
Any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a [LangServe](/docs/langserve) server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.
Whenever your LCEL chains have steps that can be executed in parallel (eg if you fetch documents from multiple retrievers) we automatically do it, both in the sync and the async interfaces, for the smallest possible latency.
[**Retries and fallbacks**](/docs/guides/productionization/fallbacks)
Configure retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We’re currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.
For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used to let end-users know something is happening, or even just to debug your chain. You can stream intermediate results, and it’s available on every [LangServe](/docs/langserve) server.
[**Input and output schemas**](/docs/expression_language/interface#input-schema)
Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.
[**Seamless LangSmith tracing**](/docs/langsmith)
As your chains get more and more complex, it becomes increasingly important to understand what exactly is happening at every step.
With LCEL, **all** steps are automatically logged to [LangSmith](/docs/langsmith/) for maximum observability and debuggability.
**LangChain** is a framework for developing applications powered by large language models (LLMs).
LangChain simplifies every stage of the LLM application lifecycle:
- **Development**: Build your applications using LangChain's open-source [building blocks](/docs/expression_language/) and [components](/docs/modules/). Hit the ground running using [third-party integrations](/docs/integrations/platforms/) and [Templates](/docs/templates).
- **Productionization**: Use [LangSmith](/docs/langsmith/) to inspect, monitor and evaluate your chains, so that you can continuously optimize and deploy with confidence.
- **Deployment**: Turn any chain into an API with [LangServe](/docs/langserve).
import ThemedImage from '@theme/ThemedImage';
<ThemedImage
alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
sources={{
light: '/svg/langchain_stack.svg',
dark: '/svg/langchain_stack_dark.svg',
}}
title="LangChain Framework Overview"
/>
Concretely, the framework consists of the following open-source libraries:
- **`langchain-core`**: Base abstractions and LangChain Expression Language.
- **`langchain-community`**: Third party integrations.
- Partner packages (e.g. **`langchain-openai`**, **`langchain-anthropic`**, etc.): Some integrations have been further split into their own lightweight packages that only depend on **`langchain-core`**.
- **`langchain`**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
- **[langgraph](/docs/langgraph)**: Build robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
- **[langserve](/docs/langserve)**: Deploy LangChain chains as REST APIs.
The broader ecosystem includes:
- **[LangSmith](/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor LLM applications and seamlessly integrates with LangChain.
## Get started
We recommend following our [Quickstart](/docs/get_started/quickstart) guide to familiarize yourself with the framework by building your first LangChain application.
[See here](/docs/get_started/installation) for instructions on how to install LangChain, set up your environment, and start building.
:::note
These docs focus on the Python LangChain library. [Head here](https://js.langchain.com) for docs on the JavaScript LangChain library.
:::
## Use cases
If you're looking to build something specific or are more of a hands-on learner, check out our [use-cases](/docs/use_cases).
They're walkthroughs and techniques for common end-to-end tasks, such as:
- [Question answering with RAG](/docs/use_cases/question_answering/)
LangChain Expression Language (LCEL) is the foundation of many of LangChain's components, and is a declarative way to compose chains. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains.
- **[Get started](/docs/expression_language/)**: LCEL and its benefits
- **[Runnable interface](/docs/expression_language/interface)**: The standard interface for LCEL objects
- **[Primitives](/docs/expression_language/primitives)**: More on the primitives LCEL includes
- and more!
## Ecosystem
### [🦜🛠️ LangSmith](/docs/langsmith)
Trace and evaluate your language model applications and intelligent agents to help you move from prototype to production.
### [🦜🕸️ LangGraph](/docs/langgraph)
Build stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain primitives.
### [🦜🏓 LangServe](/docs/langserve)
Deploy LangChain runnables and chains as REST APIs.
## [Security](/docs/security)
Read up on our [Security](/docs/security) best practices to make sure you're developing safely with LangChain.
## Additional resources
### [Components](/docs/modules/)
LangChain provides standard, extendable interfaces and integrations for many different components, including:
### [Integrations](/docs/integrations/providers/)
LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/providers/).
- Get setup with LangChain, LangSmith and LangServe
- Use the most basic and common components of LangChain: prompt templates, models, and output parsers
- Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining
- Build a simple application with LangChain
- Trace your application with LangSmith
- Serve your application with LangServe
That's a fair amount to cover! Let's dive in.
## Setup
### Jupyter Notebook
This guide (and most of the other guides in the documentation) uses [Jupyter notebooks](https://jupyter.org/) and assumes the reader is as well. Jupyter notebooks are perfect for learning how to work with LLM systems because oftentimes things can go wrong (unexpected output, API down, etc) and going through guides in an interactive environment is a great way to better understand them.
You do not NEED to go through the guide in a Jupyter Notebook, but it is recommended. See [here](https://jupyter.org/install) for instructions on how to install.
For more details, see our [Installation guide](/docs/get_started/installation).
### LangSmith
Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls.
As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent.
The best way to do this is with [LangSmith](https://smith.langchain.com).
Note that LangSmith is not needed, but it is helpful.
If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:
```shell
export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY="..."
```
## Building with LangChain
LangChain enables building application that connect external sources of data and computation to LLMs.
In this quickstart, we will walk through a few different ways of doing that.
We will start with a simple LLM chain, which just relies on information in the prompt template to respond.
Next, we will build a retrieval chain, which fetches data from a separate database and passes that into the prompt template.
We will then add in chat history, to create a conversation retrieval chain. This allows you to interact in a chat manner with this LLM, so it remembers previous questions.
Finally, we will build an agent - which utilizes an LLM to determine whether or not it needs to fetch data to answer questions.
We will cover these at a high level, but there are lot of details to all of these!
We will link to relevant docs.
## LLM Chain
We'll show how to use models available via API, like OpenAI, and local open source models, using integrations like Ollama.
<Tabs>
<TabItem value="openai" label="OpenAI" default>
First we'll need to import the LangChain x OpenAI integration package.
```shell
pip install langchain-openai
```
Accessing the API requires an API key, which you can get by creating an account and heading [here](https://platform.openai.com/account/api-keys). Once we have a key we'll want to set it as an environment variable by running:
```shell
export OPENAI_API_KEY="..."
```
We can then initialize the model:
```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
```
If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the OpenAI LLM class:
[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.
First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:
* [Download](https://ollama.ai/download)
* Fetch a model via `ollama pull llama2`
Then, make sure the Ollama server is running. After that, you can do:
```python
from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
First we'll need to import the LangChain x Anthropic package.
```shell
pip install langchain-anthropic
```
Accessing the API requires an API key, which you can get by creating an account [here](https://claude.ai/login). Once we have a key we'll want to set it as an environment variable by running:
If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the Anthropic Chat Model class:
```python
llm = ChatAnthropic(api_key="...")
```
</TabItem>
<TabItem value="cohere" label="Cohere">
First we'll need to import the Cohere SDK package.
```shell
pip install langchain-cohere
```
Accessing the API requires an API key, which you can get by creating an account and heading [here](https://dashboard.cohere.com/api-keys). Once we have a key we'll want to set it as an environment variable by running:
```shell
export COHERE_API_KEY="..."
```
We can then initialize the model:
```python
from langchain_cohere import ChatCohere
llm = ChatCohere()
```
If you'd prefer not to set an environment variable you can pass the key in directly via the `cohere_api_key` named parameter when initiating the Cohere LLM class:
```python
from langchain_cohere import ChatCohere
llm = ChatCohere(cohere_api_key="...")
```
</TabItem>
</Tabs>
Once you've installed and initialized the LLM of your choice, we can try using it!
Let's ask it what LangSmith is - this is something that wasn't present in the training data so it shouldn't have a very good response.
```python
llm.invoke("how can langsmith help with testing?")
```
We can also guide its response with a prompt template.
Prompt templates convert raw user input to better input to the LLM.
```python
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages([
("system", "You are a world class technical documentation writer."),
("user", "{input}")
])
```
We can now combine these into a simple LLM chain:
```python
chain = prompt | llm
```
We can now invoke it and ask the same question. It still won't know the answer, but it should respond in a more proper tone for a technical writer!
```python
chain.invoke({"input": "how can langsmith help with testing?"})
```
The output of a ChatModel (and therefore, of this chain) is a message. However, it's often much more convenient to work with strings. Let's add a simple output parser to convert the chat message to a string.
```python
from langchain_core.output_parsers import StrOutputParser
output_parser = StrOutputParser()
```
We can now add this to the previous chain:
```python
chain = prompt | llm | output_parser
```
We can now invoke it and ask the same question. The answer will now be a string (rather than a ChatMessage).
```python
chain.invoke({"input": "how can langsmith help with testing?"})
```
### Diving Deeper
We've now successfully set up a basic LLM chain. We only touched on the basics of prompts, models, and output parsers - for a deeper dive into everything mentioned here, see [this section of documentation](/docs/modules/model_io).
## Retrieval Chain
To properly answer the original question ("how can langsmith help with testing?"), we need to provide additional context to the LLM.
We can do this via *retrieval*.
Retrieval is useful when you have **too much data** to pass to the LLM directly.
You can then use a retriever to fetch only the most relevant pieces and pass those in.
In this process, we will look up relevant documents from a *Retriever* and then pass them into the prompt.
A Retriever can be backed by anything - a SQL table, the internet, etc - but in this instance we will populate a vector store and use that as a retriever. For more information on vectorstores, see [this documentation](/docs/modules/data_connection/vectorstores).
First, we need to load the data that we want to index. To do this, we will use the WebBaseLoader. This requires installing [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/):
```shell
pip install beautifulsoup4
```
After that, we can import and use WebBaseLoader.
```python
from langchain_community.document_loaders import WebBaseLoader
Next, we need to index it into a vectorstore. This requires a few components, namely an [embedding model](/docs/modules/data_connection/text_embedding) and a [vectorstore](/docs/modules/data_connection/vectorstores).
For embedding models, we once again provide examples for accessing via API or by running local models.
Now that we have this data indexed in a vectorstore, we will create a retrieval chain.
This chain will take an incoming question, look up relevant documents, then pass those documents along with the original question into an LLM and ask it to answer the original question.
First, let's set up the chain that takes a question and the retrieved documents and generates an answer.
```python
from langchain.chains.combine_documents import create_stuff_documents_chain
prompt = ChatPromptTemplate.from_template("""Answer the following question based only on the provided context:
We can now invoke this chain. This returns a dictionary - the response from the LLM is in the `answer` key
```python
response = retrieval_chain.invoke({"input": "how can langsmith help with testing?"})
print(response["answer"])
# LangSmith offers several features that can help with testing:...
```
This answer should be much more accurate!
### Diving Deeper
We've now successfully set up a basic retrieval chain. We only touched on the basics of retrieval - for a deeper dive into everything mentioned here, see [this section of documentation](/docs/modules/data_connection).
## Conversation Retrieval Chain
The chain we've created so far can only answer single questions. One of the main types of LLM applications that people are building are chat bots. So how do we turn this chain into one that can answer follow up questions?
We can still use the `create_retrieval_chain` function, but we need to change two things:
1. The retrieval method should now not just work on the most recent input, but rather should take the whole history into account.
2. The final LLM chain should likewise take the whole history into account
**Updating Retrieval**
In order to update retrieval, we will create a new chain. This chain will take in the most recent input (`input`) and the conversation history (`chat_history`) and use an LLM to generate a search query.
```python
from langchain.chains import create_history_aware_retriever
from langchain_core.prompts import MessagesPlaceholder
# First we need a prompt that we can pass into an LLM to generate this search query
We can test this out by passing in an instance where the user asks a follow-up question.
```python
from langchain_core.messages import HumanMessage, AIMessage
chat_history = [HumanMessage(content="Can LangSmith help test my LLM applications?"), AIMessage(content="Yes!")]
retriever_chain.invoke({
"chat_history": chat_history,
"input": "Tell me how"
})
```
You should see that this returns documents about testing in LangSmith. This is because the LLM generated a new query, combining the chat history with the follow-up question.
Now that we have this new retriever, we can create a new chain to continue the conversation with these retrieved documents in mind.
```python
prompt = ChatPromptTemplate.from_messages([
("system", "Answer the user's questions based on the below context:\n\n{context}"),
chat_history = [HumanMessage(content="Can LangSmith help test my LLM applications?"), AIMessage(content="Yes!")]
retrieval_chain.invoke({
"chat_history": chat_history,
"input": "Tell me how"
})
```
We can see that this gives a coherent answer - we've successfully turned our retrieval chain into a chatbot!
## Agent
We've so far created examples of chains - where each step is known ahead of time.
The final thing we will create is an agent - where the LLM decides what steps to take.
**NOTE: for this example we will only show how to create an agent using OpenAI models, as local models are not reliable enough yet.**
One of the first things to do when building an agent is to decide what tools it should have access to.
For this example, we will give the agent access to two tools:
1. The retriever we just created. This will let it easily answer questions about LangSmith
2. A search tool. This will let it easily answer questions that require up-to-date information.
First, let's set up a tool for the retriever we just created:
```python
from langchain.tools.retriever import create_retriever_tool
retriever_tool = create_retriever_tool(
retriever,
"langsmith_search",
"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)
```
The search tool that we will use is [Tavily](/docs/integrations/retrievers/tavily). This will require an API key (they have generous free tier). After creating it on their platform, you need to set it as an environment variable:
```shell
export TAVILY_API_KEY=...
```
If you do not want to set up an API key, you can skip creating this tool.
```python
from langchain_community.tools.tavily_search import TavilySearchResults
search = TavilySearchResults()
```
We can now create a list of the tools we want to work with:
```python
tools = [retriever_tool, search]
```
Now that we have the tools, we can create an agent to use them. We will go over this pretty quickly - for a deeper dive into what exactly is going on, check out the [Agent's Getting Started documentation](/docs/modules/agents)
Install langchain hub first
```bash
pip install langchainhub
```
Install the langchain-openai package
To interact with OpenAI we need to use langchain-openai which connects with OpenAI SDK[https://github.com/langchain-ai/langchain/tree/master/libs/partners/openai].
```bash
pip install langchain-openai
```
Now we can use it to get a predefined prompt
```python
from langchain_openai import ChatOpenAI
from langchain import hub
from langchain.agents import create_openai_functions_agent
We can now invoke the agent and see how it responds! We can ask it questions about LangSmith:
```python
agent_executor.invoke({"input": "how can langsmith help with testing?"})
```
We can ask it about the weather:
```python
agent_executor.invoke({"input": "what is the weather in SF?"})
```
We can have conversations with it:
```python
chat_history = [HumanMessage(content="Can LangSmith help test my LLM applications?"), AIMessage(content="Yes!")]
agent_executor.invoke({
"chat_history": chat_history,
"input": "Tell me how"
})
```
### Diving Deeper
We've now successfully set up a basic agent. We only touched on the basics of agents - for a deeper dive into everything mentioned here, see [this section of documentation](/docs/modules/agents).
## Serving with LangServe
Now that we've built an application, we need to serve it. That's where LangServe comes in.
LangServe helps developers deploy LangChain chains as a REST API. You do not need to use LangServe to use LangChain, but in this guide we'll show how you can deploy your app with LangServe.
While the first part of this guide was intended to be run in a Jupyter Notebook, we will now move out of that. We will be creating a Python file and then interacting with it from the command line.
Install with:
```bash
pip install "langserve[all]"
```
### Server
To create a server for our application we'll make a `serve.py` file. This will contain our logic for serving our application. It consists of three things:
1. The definition of our chain that we just built above
2. Our FastAPI app
3. A definition of a route from which to serve the chain, which is done with `langserve.add_routes`
```python
#!/usr/bin/env python
from typing import List
from fastapi import FastAPI
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_community.document_loaders import WebBaseLoader
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.tools.retriever import create_retriever_tool
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
from langchain.pydantic_v1 import BaseModel, Field
we should see our chain being served at localhost:8000.
### Playground
Every LangServe service comes with a simple built-in UI for configuring and invoking the application with streaming output and visibility into intermediate steps.
Head to http://localhost:8000/agent/playground/ to try it out! Pass in the same question as before - "how can langsmith help with testing?" - and it should respond same as before.
### Client
Now let's set up a client for programmatically interacting with our service. We can easily do this with the `[langserve.RemoteRunnable](/docs/langserve#client)`.
Using this, we can interact with the served chain as if it were running client-side.
"chat_history": [] # Providing an empty list as this is the first call
})
```
To learn more about the many other features of LangServe [head here](/docs/langserve).
## Next steps
We've touched on how to build an application with LangChain, how to trace it with LangSmith, and how to serve it with LangServe.
There are a lot more features in all three of these than we can cover here.
To continue on your journey, we recommend you read the following (in order):
- All of these features are backed by [LangChain Expression Language (LCEL)](/docs/expression_language) - a way to chain these components together. Check out that documentation to better understand how to create custom chains.
- [Model IO](/docs/modules/model_io) covers more details of prompts, LLMs, and output parsers.
- [Retrieval](/docs/modules/data_connection) covers more details of everything related to retrieval
- [Agents](/docs/modules/agents) covers details of everything related to agents
- Explore common [end-to-end use cases](/docs/use_cases/) and [template applications](/docs/templates)
- [Read up on LangSmith](/docs/langsmith/), the platform for debugging, testing, monitoring and more
- Learn more about serving your applications with [LangServe](/docs/langserve)
If you're building with LLMs, at some point something will break, and you'll need to debug. A model call will fail, or the model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created.
Here are a few different tools and functionalities to aid in debugging.
## Tracing
Platforms with tracing capabilities like [LangSmith](/docs/langsmith/) are the most comprehensive solutions for debugging. These platforms make it easy to not only log and visualize LLM apps, but also to actively debug, test and refine them.
When building production-grade LLM applications, platforms like this are essential.
## `set_debug` and `set_verbose`
If you're prototyping in Jupyter Notebooks or running Python scripts, it can be helpful to print out the intermediate steps of a Chain run.
There are a number of ways to enable printing at varying degrees of verbosity.
Let's suppose we have a simple agent, and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see:
agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?")
```
<CodeOutputBlock lang="python">
```
'The director of the 2023 film Oppenheimer is Christopher Nolan and he is approximately 19345 days old in 2023.'
```
</CodeOutputBlock>
### `set_debug(True)`
Setting the global `debug` flag will cause all LangChain components with callback support (chains, models, agents, tools, retrievers) to print the inputs they receive and outputs they generate. This is the most verbose setting and will fully log raw inputs and outputs.
```python
fromlangchain.globalsimportset_debug
set_debug(True)
agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?")
```
<details> <summary>Console output</summary>
<CodeOutputBlock lang="python">
```
[chain/start] [1:RunTypeEnum.chain:AgentExecutor] Entering Chain run with input:
{
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?"
}
[chain/start] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain] Entering Chain run with input:
{
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"agent_scratchpad": "",
"stop": [
"\nObservation:",
"\n\tObservation:"
]
}
[llm/start] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain > 3:RunTypeEnum.llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"Human: Answer the following questions as best you can. You have access to the following tools:\n\nduckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [duckduckgo_search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?\nThought:"
]
}
[llm/end] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain > 3:RunTypeEnum.llm:ChatOpenAI] [5.53s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"",
"generation_info": {
"finish_reason": "stop"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"",
"additional_kwargs": {}
}
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 206,
"completion_tokens": 71,
"total_tokens": 277
},
"model_name": "gpt-4"
},
"run": null
}
[chain/end] [1:RunTypeEnum.chain:AgentExecutor > 2:RunTypeEnum.chain:LLMChain] [5.53s] Exiting Chain run with output:
{
"text": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\""
}
[tool/start] [1:RunTypeEnum.chain:AgentExecutor > 4:RunTypeEnum.tool:duckduckgo_search] Entering Tool run with input:
"Director of the 2023 film Oppenheimer and their age"
[tool/end] [1:RunTypeEnum.chain:AgentExecutor > 4:RunTypeEnum.tool:duckduckgo_search] [1.51s] Exiting Tool run with output:
"Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age."
[chain/start] [1:RunTypeEnum.chain:AgentExecutor > 5:RunTypeEnum.chain:LLMChain] Entering Chain run with input:
{
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"agent_scratchpad": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"\nObservation: Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, \"Oppenheimer,\" Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.\nThought:",
"stop": [
"\nObservation:",
"\n\tObservation:"
]
}
[llm/start] [1:RunTypeEnum.chain:AgentExecutor > 5:RunTypeEnum.chain:LLMChain > 6:RunTypeEnum.llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"Human: Answer the following questions as best you can. You have access to the following tools:\n\nduckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [duckduckgo_search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?\nThought:I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"\nObservation: Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, \"Oppenheimer,\" Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.\nThought:"
]
}
[llm/end] [1:RunTypeEnum.chain:AgentExecutor > 5:RunTypeEnum.chain:LLMChain > 6:RunTypeEnum.llm:ChatOpenAI] [4.46s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\"",
"generation_info": {
"finish_reason": "stop"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\"",
"additional_kwargs": {}
}
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 550,
"completion_tokens": 39,
"total_tokens": 589
},
"model_name": "gpt-4"
},
"run": null
}
[chain/end] [1:RunTypeEnum.chain:AgentExecutor > 5:RunTypeEnum.chain:LLMChain] [4.46s] Exiting Chain run with output:
{
"text": "The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\""
}
[tool/start] [1:RunTypeEnum.chain:AgentExecutor > 7:RunTypeEnum.tool:duckduckgo_search] Entering Tool run with input:
"Christopher Nolan age"
[tool/end] [1:RunTypeEnum.chain:AgentExecutor > 7:RunTypeEnum.tool:duckduckgo_search] [1.33s] Exiting Tool run with output:
"Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film July 11, 2023 5 AM PT For Subscribers Christopher Nolan is photographed in Los Angeles. (Joe Pugliese / For The Times) This is not the story I was supposed to write. Oppenheimer director Christopher Nolan, Cillian Murphy, Emily Blunt and Matt Damon on the stakes of making a three-hour, CGI-free summer film. Christopher Nolan, the director behind such films as "Dunkirk," "Inception," "Interstellar," and the "Dark Knight" trilogy, has spent the last three years living in Oppenheimer's world, writing ..."
[chain/start] [1:RunTypeEnum.chain:AgentExecutor > 8:RunTypeEnum.chain:LLMChain] Entering Chain run with input:
{
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"agent_scratchpad": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"\nObservation: Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, \"Oppenheimer,\" Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.\nThought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\"\nObservation: Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. July 30, 1970 (age 52) London England Notable Works: \"Dunkirk\" \"Tenet\" \"The Prestige\" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film July 11, 2023 5 AM PT For Subscribers Christopher Nolan is photographed in Los Angeles. (Joe Pugliese / For The Times) This is not the story I was supposed to write. Oppenheimer director Christopher Nolan, Cillian Murphy, Emily Blunt and Matt Damon on the stakes of making a three-hour, CGI-free summer film. Christopher Nolan, the director behind such films as \"Dunkirk,\" \"Inception,\" \"Interstellar,\" and the \"Dark Knight\" trilogy, has spent the last three years living in Oppenheimer's world, writing ...\nThought:",
"stop": [
"\nObservation:",
"\n\tObservation:"
]
}
[llm/start] [1:RunTypeEnum.chain:AgentExecutor > 8:RunTypeEnum.chain:LLMChain > 9:RunTypeEnum.llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"Human: Answer the following questions as best you can. You have access to the following tools:\n\nduckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [duckduckgo_search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?\nThought:I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"\nObservation: Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, \"Oppenheimer,\" Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.\nThought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\"\nObservation: Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. July 30, 1970 (age 52) London England Notable Works: \"Dunkirk\" \"Tenet\" \"The Prestige\" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film July 11, 2023 5 AM PT For Subscribers Christopher Nolan is photographed in Los Angeles. (Joe Pugliese / For The Times) This is not the story I was supposed to write. Oppenheimer director Christopher Nolan, Cillian Murphy, Emily Blunt and Matt Damon on the stakes of making a three-hour, CGI-free summer film. Christopher Nolan, the director behind such films as \"Dunkirk,\" \"Inception,\" \"Interstellar,\" and the \"Dark Knight\" trilogy, has spent the last three years living in Oppenheimer's world, writing ...\nThought:"
]
}
[llm/end] [1:RunTypeEnum.chain:AgentExecutor > 8:RunTypeEnum.chain:LLMChain > 9:RunTypeEnum.llm:ChatOpenAI] [2.69s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "Christopher Nolan was born on July 30, 1970, which makes him 52 years old in 2023. Now I need to calculate his age in days.\nAction: Calculator\nAction Input: 52*365",
"generation_info": {
"finish_reason": "stop"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "Christopher Nolan was born on July 30, 1970, which makes him 52 years old in 2023. Now I need to calculate his age in days.\nAction: Calculator\nAction Input: 52*365",
"additional_kwargs": {}
}
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 868,
"completion_tokens": 46,
"total_tokens": 914
},
"model_name": "gpt-4"
},
"run": null
}
[chain/end] [1:RunTypeEnum.chain:AgentExecutor > 8:RunTypeEnum.chain:LLMChain] [2.69s] Exiting Chain run with output:
{
"text": "Christopher Nolan was born on July 30, 1970, which makes him 52 years old in 2023. Now I need to calculate his age in days.\nAction: Calculator\nAction Input: 52*365"
}
[tool/start] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator] Entering Tool run with input:
"52*365"
[chain/start] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator > 11:RunTypeEnum.chain:LLMMathChain] Entering Chain run with input:
{
"question": "52*365"
}
[chain/start] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator > 11:RunTypeEnum.chain:LLMMathChain > 12:RunTypeEnum.chain:LLMChain] Entering Chain run with input:
{
"question": "52*365",
"stop": [
"```output"
]
}
[llm/start] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator > 11:RunTypeEnum.chain:LLMMathChain > 12:RunTypeEnum.chain:LLMChain > 13:RunTypeEnum.llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"Human: Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${Question with math problem.}\n```text\n${single line mathematical expression that solves the problem}\n```\n...numexpr.evaluate(text)...\n```output\n${Output of running the code}\n```\nAnswer: ${Answer}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate(\"37593 * 67\")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate(\"37593**(1/5)\")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: 52*365"
]
}
[llm/end] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator > 11:RunTypeEnum.chain:LLMMathChain > 12:RunTypeEnum.chain:LLMChain > 13:RunTypeEnum.llm:ChatOpenAI] [2.89s] Exiting LLM run with output:
[chain/end] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator > 11:RunTypeEnum.chain:LLMMathChain] [2.90s] Exiting Chain run with output:
{
"answer": "Answer: 18980"
}
[tool/end] [1:RunTypeEnum.chain:AgentExecutor > 10:RunTypeEnum.tool:Calculator] [2.90s] Exiting Tool run with output:
"Answer: 18980"
[chain/start] [1:RunTypeEnum.chain:AgentExecutor > 14:RunTypeEnum.chain:LLMChain] Entering Chain run with input:
{
"input": "Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?",
"agent_scratchpad": "I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"\nObservation: Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, \"Oppenheimer,\" Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.\nThought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\"\nObservation: Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. July 30, 1970 (age 52) London England Notable Works: \"Dunkirk\" \"Tenet\" \"The Prestige\" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film July 11, 2023 5 AM PT For Subscribers Christopher Nolan is photographed in Los Angeles. (Joe Pugliese / For The Times) This is not the story I was supposed to write. Oppenheimer director Christopher Nolan, Cillian Murphy, Emily Blunt and Matt Damon on the stakes of making a three-hour, CGI-free summer film. Christopher Nolan, the director behind such films as \"Dunkirk,\" \"Inception,\" \"Interstellar,\" and the \"Dark Knight\" trilogy, has spent the last three years living in Oppenheimer's world, writing ...\nThought:Christopher Nolan was born on July 30, 1970, which makes him 52 years old in 2023. Now I need to calculate his age in days.\nAction: Calculator\nAction Input: 52*365\nObservation: Answer: 18980\nThought:",
"stop": [
"\nObservation:",
"\n\tObservation:"
]
}
[llm/start] [1:RunTypeEnum.chain:AgentExecutor > 14:RunTypeEnum.chain:LLMChain > 15:RunTypeEnum.llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"Human: Answer the following questions as best you can. You have access to the following tools:\n\nduckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [duckduckgo_search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?\nThought:I need to find out who directed the 2023 film Oppenheimer and their age. Then, I need to calculate their age in days. I will use DuckDuckGo to find out the director and their age.\nAction: duckduckgo_search\nAction Input: \"Director of the 2023 film Oppenheimer and their age\"\nObservation: Capturing the mad scramble to build the first atomic bomb required rapid-fire filming, strict set rules and the construction of an entire 1940s western town. By Jada Yuan. July 19, 2023 at 5:00 a ... In Christopher Nolan's new film, \"Oppenheimer,\" Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. Christopher Nolan goes deep on 'Oppenheimer,' his most 'extreme' film to date. By Kenneth Turan. July 11, 2023 5 AM PT. For Subscribers. Christopher Nolan is photographed in Los Angeles ... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.\nThought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his age.\nAction: duckduckgo_search\nAction Input: \"Christopher Nolan age\"\nObservation: Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. July 30, 1970 (age 52) London England Notable Works: \"Dunkirk\" \"Tenet\" \"The Prestige\" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film July 11, 2023 5 AM PT For Subscribers Christopher Nolan is photographed in Los Angeles. (Joe Pugliese / For The Times) This is not the story I was supposed to write. Oppenheimer director Christopher Nolan, Cillian Murphy, Emily Blunt and Matt Damon on the stakes of making a three-hour, CGI-free summer film. Christopher Nolan, the director behind such films as \"Dunkirk,\" \"Inception,\" \"Interstellar,\" and the \"Dark Knight\" trilogy, has spent the last three years living in Oppenheimer's world, writing ...\nThought:Christopher Nolan was born on July 30, 1970, which makes him 52 years old in 2023. Now I need to calculate his age in days.\nAction: Calculator\nAction Input: 52*365\nObservation: Answer: 18980\nThought:"
]
}
[llm/end] [1:RunTypeEnum.chain:AgentExecutor > 14:RunTypeEnum.chain:LLMChain > 15:RunTypeEnum.llm:ChatOpenAI] [3.52s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "I now know the final answer\nFinal Answer: The director of the 2023 film Oppenheimer is Christopher Nolan and he is 52 years old. His age in days is approximately 18980 days.",
"generation_info": {
"finish_reason": "stop"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "I now know the final answer\nFinal Answer: The director of the 2023 film Oppenheimer is Christopher Nolan and he is 52 years old. His age in days is approximately 18980 days.",
"additional_kwargs": {}
}
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 926,
"completion_tokens": 43,
"total_tokens": 969
},
"model_name": "gpt-4"
},
"run": null
}
[chain/end] [1:RunTypeEnum.chain:AgentExecutor > 14:RunTypeEnum.chain:LLMChain] [3.52s] Exiting Chain run with output:
{
"text": "I now know the final answer\nFinal Answer: The director of the 2023 film Oppenheimer is Christopher Nolan and he is 52 years old. His age in days is approximately 18980 days."
}
[chain/end] [1:RunTypeEnum.chain:AgentExecutor] [21.96s] Exiting Chain run with output:
{
"output": "The director of the 2023 film Oppenheimer is Christopher Nolan and he is 52 years old. His age in days is approximately 18980 days."
}
'The director of the 2023 film Oppenheimer is Christopher Nolan and he is 52 years old. His age in days is approximately 18980 days.'
```
</CodeOutputBlock>
</details>
### `set_verbose(True)`
Setting the `verbose` flag will print out inputs and outputs in a slightly more readable format and will skip logging certain raw outputs (like the token usage stats for an LLM call) so that you can focus on application logic.
```python
fromlangchain.globalsimportset_verbose
set_verbose(True)
agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?")
```
<details> <summary>Console output</summary>
<CodeOutputBlock lang="python">
```
> Entering new AgentExecutor chain...
> Entering new LLMChain chain...
Prompt after formatting:
Answer the following questions as best you can. You have access to the following tools:
duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
Calculator: Useful for when you need to answer questions about math.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [duckduckgo_search, Calculator]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
Thought:
> Finished chain.
First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
Action: duckduckgo_search
Action Input: "Director of the 2023 film Oppenheimer"
Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert ... 2023, 12:16 p.m. ET. ... including his role as the director of the Manhattan Engineer District, better ... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". In this opening salvo of 2023's Oscar battle, Nolan has enjoined a star-studded cast for a retelling of the brilliant and haunted life of J. Robert Oppenheimer, the American physicist whose... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.
Thought:
> Entering new LLMChain chain...
Prompt after formatting:
Answer the following questions as best you can. You have access to the following tools:
duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
Calculator: Useful for when you need to answer questions about math.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [duckduckgo_search, Calculator]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
Thought:First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
Action: duckduckgo_search
Action Input: "Director of the 2023 film Oppenheimer"
Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert ... 2023, 12:16 p.m. ET. ... including his role as the director of the Manhattan Engineer District, better ... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". In this opening salvo of 2023's Oscar battle, Nolan has enjoined a star-studded cast for a retelling of the brilliant and haunted life of J. Robert Oppenheimer, the American physicist whose... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.
Thought:
> Finished chain.
The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his birth date to calculate his age.
Action: duckduckgo_search
Action Input: "Christopher Nolan birth date"
Observation: July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. Christopher Nolan is currently 52 according to his birthdate July 30, 1970 Sun Sign Leo Born Place Westminster, London, England, United Kingdom Residence Los Angeles, California, United States Nationality Education Chris attended Haileybury and Imperial Service College, in Hertford Heath, Hertfordshire. Christopher Nolan's next movie will study the man who developed the atomic bomb, J. Robert Oppenheimer. Here's the release date, plot, trailers & more. July 2023 sees the release of Christopher Nolan's new film, Oppenheimer, his first movie since 2020's Tenet and his split from Warner Bros. Billed as an epic thriller about "the man who ...
Thought:
> Entering new LLMChain chain...
Prompt after formatting:
Answer the following questions as best you can. You have access to the following tools:
duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
Calculator: Useful for when you need to answer questions about math.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [duckduckgo_search, Calculator]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
Thought:First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
Action: duckduckgo_search
Action Input: "Director of the 2023 film Oppenheimer"
Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert ... 2023, 12:16 p.m. ET. ... including his role as the director of the Manhattan Engineer District, better ... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". In this opening salvo of 2023's Oscar battle, Nolan has enjoined a star-studded cast for a retelling of the brilliant and haunted life of J. Robert Oppenheimer, the American physicist whose... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.
Thought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his birth date to calculate his age.
Action: duckduckgo_search
Action Input: "Christopher Nolan birth date"
Observation: July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. Christopher Nolan is currently 52 according to his birthdate July 30, 1970 Sun Sign Leo Born Place Westminster, London, England, United Kingdom Residence Los Angeles, California, United States Nationality Education Chris attended Haileybury and Imperial Service College, in Hertford Heath, Hertfordshire. Christopher Nolan's next movie will study the man who developed the atomic bomb, J. Robert Oppenheimer. Here's the release date, plot, trailers & more. July 2023 sees the release of Christopher Nolan's new film, Oppenheimer, his first movie since 2020's Tenet and his split from Warner Bros. Billed as an epic thriller about "the man who ...
Thought:
> Finished chain.
Christopher Nolan was born on July 30, 1970. Now I need to calculate his age in 2023 and then convert it into days.
Action: Calculator
Action Input: (2023 - 1970) * 365
> Entering new LLMMathChain chain...
(2023 - 1970) * 365
> Entering new LLMChain chain...
Prompt after formatting:
Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.
Question: ${Question with math problem.}
```text
${single line mathematical expression that solves the problem}
```
...numexpr.evaluate(text)...
```output
${Output of running the code}
```
Answer: ${Answer}
Begin.
Question: What is 37593 * 67?
```text
37593 * 67
```
...numexpr.evaluate("37593 * 67")...
```output
2518731
```
Answer: 2518731
Question: 37593^(1/5)
```text
37593**(1/5)
```
...numexpr.evaluate("37593**(1/5)")...
```output
8.222831614237718
```
Answer: 8.222831614237718
Question: (2023 - 1970) * 365
> Finished chain.
```text
(2023 - 1970) * 365
```
...numexpr.evaluate("(2023 - 1970) * 365")...
Answer: 19345
> Finished chain.
Observation: Answer: 19345
Thought:
> Entering new LLMChain chain...
Prompt after formatting:
Answer the following questions as best you can. You have access to the following tools:
duckduckgo_search: A wrapper around DuckDuckGo Search. Useful for when you need to answer questions about current events. Input should be a search query.
Calculator: Useful for when you need to answer questions about math.
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [duckduckgo_search, Calculator]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?
Thought:First, I need to find out who directed the film Oppenheimer in 2023 and their birth date to calculate their age.
Action: duckduckgo_search
Action Input: "Director of the 2023 film Oppenheimer"
Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert ... 2023, 12:16 p.m. ET. ... including his role as the director of the Manhattan Engineer District, better ... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". In this opening salvo of 2023's Oscar battle, Nolan has enjoined a star-studded cast for a retelling of the brilliant and haunted life of J. Robert Oppenheimer, the American physicist whose... Oppenheimer is a 2023 epic biographical thriller film written and directed by Christopher Nolan.It is based on the 2005 biography American Prometheus by Kai Bird and Martin J. Sherwin about J. Robert Oppenheimer, a theoretical physicist who was pivotal in developing the first nuclear weapons as part of the Manhattan Project and thereby ushering in the Atomic Age.
Thought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his birth date to calculate his age.
Action: duckduckgo_search
Action Input: "Christopher Nolan birth date"
Observation: July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. Christopher Nolan is currently 52 according to his birthdate July 30, 1970 Sun Sign Leo Born Place Westminster, London, England, United Kingdom Residence Los Angeles, California, United States Nationality Education Chris attended Haileybury and Imperial Service College, in Hertford Heath, Hertfordshire. Christopher Nolan's next movie will study the man who developed the atomic bomb, J. Robert Oppenheimer. Here's the release date, plot, trailers & more. July 2023 sees the release of Christopher Nolan's new film, Oppenheimer, his first movie since 2020's Tenet and his split from Warner Bros. Billed as an epic thriller about "the man who ...
Thought:Christopher Nolan was born on July 30, 1970. Now I need to calculate his age in 2023 and then convert it into days.
Action: Calculator
Action Input: (2023 - 1970) * 365
Observation: Answer: 19345
Thought:
> Finished chain.
I now know the final answer
Final Answer: The director of the 2023 film Oppenheimer is Christopher Nolan and he is 53 years old in 2023. His age in days is 19345 days.
> Finished chain.
'The director of the 2023 film Oppenheimer is Christopher Nolan and he is 53 years old in 2023. His age in days is 19345 days.'
```
</CodeOutputBlock>
</details>
### `Chain(..., verbose=True)`
You can also scope verbosity down to a single object, in which case only the inputs and outputs to that object are printed (along with any additional callbacks calls made specifically by that object).
```python
# Passing verbose=True to initialize_agent will pass that along to the AgentExecutor (which is a Chain).
agent=initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is their age in days (assume 365 days per year)?")
```
<details> <summary>Console output</summary>
<CodeOutputBlock lang="python">
```
> Entering new AgentExecutor chain...
First, I need to find out who directed the film Oppenheimer in 2023 and their birth date. Then, I can calculate their age in years and days.
Action: duckduckgo_search
Action Input: "Director of 2023 film Oppenheimer"
Observation: Oppenheimer: Directed by Christopher Nolan. With Cillian Murphy, Emily Blunt, Robert Downey Jr., Alden Ehrenreich. The story of American scientist J. Robert Oppenheimer and his role in the development of the atomic bomb. In Christopher Nolan's new film, "Oppenheimer," Cillian Murphy stars as J. Robert Oppenheimer, the American physicist who oversaw the Manhattan Project in Los Alamos, N.M. Universal Pictures... J Robert Oppenheimer was the director of the secret Los Alamos Laboratory. It was established under US president Franklin D Roosevelt as part of the Manhattan Project to build the first atomic bomb. He oversaw the first atomic bomb detonation in the New Mexico desert in July 1945, code-named "Trinity". A Review of Christopher Nolan's new film 'Oppenheimer' , the story of the man who fathered the Atomic Bomb. Cillian Murphy leads an all star cast ... Release Date: July 21, 2023. Director ... For his new film, "Oppenheimer," starring Cillian Murphy and Emily Blunt, director Christopher Nolan set out to build an entire 1940s western town.
Thought:The director of the 2023 film Oppenheimer is Christopher Nolan. Now I need to find out his birth date to calculate his age.
Action: duckduckgo_search
Action Input: "Christopher Nolan birth date"
Observation: July 30, 1970 (age 52) London England Notable Works: "Dunkirk" "Tenet" "The Prestige" See all related content → Recent News Jul. 13, 2023, 11:11 AM ET (AP) Cillian Murphy, playing Oppenheimer, finally gets to lead a Christopher Nolan film Christopher Edward Nolan CBE (born 30 July 1970) is a British and American filmmaker. Known for his Hollywood blockbusters with complex storytelling, Nolan is considered a leading filmmaker of the 21st century. His films have grossed $5 billion worldwide. The recipient of many accolades, he has been nominated for five Academy Awards, five BAFTA Awards and six Golden Globe Awards. Christopher Nolan is currently 52 according to his birthdate July 30, 1970 Sun Sign Leo Born Place Westminster, London, England, United Kingdom Residence Los Angeles, California, United States Nationality Education Chris attended Haileybury and Imperial Service College, in Hertford Heath, Hertfordshire. Christopher Nolan's next movie will study the man who developed the atomic bomb, J. Robert Oppenheimer. Here's the release date, plot, trailers & more. Date of Birth: 30 July 1970 . ... Christopher Nolan is a British-American film director, producer, and screenwriter. His films have grossed more than US$5 billion worldwide, and have garnered 11 Academy Awards from 36 nominations. ...
Thought:Christopher Nolan was born on July 30, 1970. Now I can calculate his age in years and then in days.
Final Answer: The director of the 2023 film Oppenheimer is Christopher Nolan. He is 53 years old in 2023, which is approximately 19345 days.
> Finished chain.
'The director of the 2023 film Oppenheimer is Christopher Nolan. He is 53 years old in 2023, which is approximately 19345 days.'
```
</CodeOutputBlock>
</details>
## Other callbacks
`Callbacks` are what we use to execute any functionality within a component outside the primary component logic. All of the above solutions use `Callbacks` under the hood to log intermediate steps of components. There are a number of `Callbacks` relevant for debugging that come with LangChain out of the box, like the [FileCallbackHandler](/docs/modules/callbacks/filecallbackhandler). You can also implement your own callbacks to execute custom functionality.
See here for more info on [Callbacks](/docs/modules/callbacks/), how to use them, and customize them.
Extending LangChain's base abstractions, whether you're planning to contribute back to the open-source repo or build a bespoke internal integration, is encouraged.
Check out these guides for building your own custom classes for the following modules:
- [Chat models](/docs/modules/model_io/chat/custom_chat_model) for interfacing with chat-tuned language models.
- [LLMs](/docs/modules/model_io/llms/custom_llm) for interfacing with text language models.
- [Output parsers](/docs/modules/model_io/output_parsers/custom) for handling language model outputs.
In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it is crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories:
In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc.
- **Case 2: Self-hosted Open-Source Models**
Alternatively, developers can opt to use smaller, yet comparably capable, self-hosted open-source LLM models. This approach can significantly decrease costs, latency, and privacy concerns associated with transferring data to external LLM providers.
Regardless of the framework that forms the backbone of your product, deploying LLM applications comes with its own set of challenges. It's vital to understand the trade-offs and key considerations when evaluating serving frameworks.
## Outline
This guide aims to provide a comprehensive overview of the requirements for deploying LLMs in a production setting, focusing on:
- **Designing a Robust LLM Application Service**
- **Maintaining Cost-Efficiency**
- **Ensuring Rapid Iteration**
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.
## Designing a Robust LLM Application Service
When deploying an LLM service in production, it's imperative to provide a seamless user experience free from outages. Achieving 24/7 service availability involves creating and maintaining several sub-systems surrounding your application.
### Monitoring
Monitoring forms an integral part of any system running in a production environment. In the context of LLMs, it is essential to monitor both performance and quality metrics.
**Performance Metrics:** These metrics provide insights into the efficiency and capacity of your model. Here are some key examples:
- Query per second (QPS): This measures the number of queries your model processes in a second, offering insights into its utilization.
- Latency: This metric quantifies the delay from when your client sends a request to when they receive a response.
- Tokens Per Second (TPS): This represents the number of tokens your model can generate in a second.
**Quality Metrics:** These metrics are typically customized according to the business use-case. For instance, how does the output of your system compare to a baseline, such as a previous version? Although these metrics can be calculated offline, you need to log the necessary data to use them later.
### Fault tolerance
Your application may encounter errors such as exceptions in your model inference or business logic code, causing failures and disrupting traffic. Other potential issues could arise from the machine running your application, such as unexpected hardware breakdowns or loss of spot-instances during high-demand periods. One way to mitigate these risks is by increasing redundancy through replica scaling and implementing recovery mechanisms for failed replicas. However, model replicas aren't the only potential points of failure. It's essential to build resilience against various failures that could occur at any point in your stack.
### Zero down time upgrade
System upgrades are often necessary but can result in service disruptions if not handled correctly. One way to prevent downtime during upgrades is by implementing a smooth transition process from the old version to the new one. Ideally, the new version of your LLM service is deployed, and traffic gradually shifts from the old to the new version, maintaining a constant QPS throughout the process.
### Load balancing
Load balancing, in simple terms, is a technique to distribute work evenly across multiple computers, servers, or other resources to optimize the utilization of the system, maximize throughput, minimize response time, and avoid overload of any single resource. Think of it as a traffic officer directing cars (requests) to different roads (servers) so that no single road becomes too congested.
There are several strategies for load balancing. For example, one common method is the *Round Robin* strategy, where each request is sent to the next server in line, cycling back to the first when all servers have received a request. This works well when all servers are equally capable. However, if some servers are more powerful than others, you might use a *Weighted Round Robin* or *Least Connections* strategy, where more requests are sent to the more powerful servers, or to those currently handling the fewest active requests. Let's imagine you're running a LLM chain. If your application becomes popular, you could have hundreds or even thousands of users asking questions at the same time. If one server gets too busy (high load), the load balancer would direct new requests to another server that is less busy. This way, all your users get a timely response and the system remains stable.
## Maintaining Cost-Efficiency and Scalability
Deploying LLM services can be costly, especially when you're handling a large volume of user interactions. Charges by LLM providers are usually based on tokens used, making a chat system inference on these models potentially expensive. However, several strategies can help manage these costs without compromising the quality of the service.
### Self-hosting models
Several smaller and open-source LLMs are emerging to tackle the issue of reliance on LLM providers. Self-hosting allows you to maintain similar quality to LLM provider models while managing costs. The challenge lies in building a reliable, high-performing LLM serving system on your own machines.
### Resource Management and Auto-Scaling
Computational logic within your application requires precise resource allocation. For instance, if part of your traffic is served by an OpenAI endpoint and another part by a self-hosted model, it's crucial to allocate suitable resources for each. Auto-scaling—adjusting resource allocation based on traffic—can significantly impact the cost of running your application. This strategy requires a balance between cost and responsiveness, ensuring neither resource over-provisioning nor compromised application responsiveness.
### Utilizing Spot Instances
On platforms like AWS, spot instances offer substantial cost savings, typically priced at about a third of on-demand instances. The trade-off is a higher crash rate, necessitating a robust fault-tolerance mechanism for effective use.
### Independent Scaling
When self-hosting your models, you should consider independent scaling. For example, if you have two translation models, one fine-tuned for French and another for Spanish, incoming requests might necessitate different scaling requirements for each.
### Batching requests
In the context of Large Language Models, batching requests can enhance efficiency by better utilizing your GPU resources. GPUs are inherently parallel processors, designed to handle multiple tasks simultaneously. If you send individual requests to the model, the GPU might not be fully utilized as it's only working on a single task at a time. On the other hand, by batching requests together, you're allowing the GPU to work on multiple tasks at once, maximizing its utilization and improving inference speed. This not only leads to cost savings but can also improve the overall latency of your LLM service.
In summary, managing costs while scaling your LLM services requires a strategic approach. Utilizing self-hosting models, managing resources effectively, employing auto-scaling, using spot instances, independently scaling models, and batching requests are key strategies to consider. Open-source libraries such as Ray Serve and BentoML are designed to deal with these complexities.
## Ensuring Rapid Iteration
The LLM landscape is evolving at an unprecedented pace, with new libraries and model architectures being introduced constantly. Consequently, it's crucial to avoid tying yourself to a solution specific to one particular framework. This is especially relevant in serving, where changes to your infrastructure can be time-consuming, expensive, and risky. Strive for infrastructure that is not locked into any specific machine learning library or framework, but instead offers a general-purpose, scalable serving layer. Here are some aspects where flexibility plays a key role:
### Model composition
Deploying systems like LangChain demands the ability to piece together different models and connect them via logic. Take the example of building a natural language input SQL query engine. Querying an LLM and obtaining the SQL command is only part of the system. You need to extract metadata from the connected database, construct a prompt for the LLM, run the SQL query on an engine, collect and feedback the response to the LLM as the query runs, and present the results to the user. This demonstrates the need to seamlessly integrate various complex components built in Python into a dynamic chain of logical blocks that can be served together.
## Cloud providers
Many hosted solutions are restricted to a single cloud provider, which can limit your options in today's multi-cloud world. Depending on where your other infrastructure components are built, you might prefer to stick with your chosen cloud provider.
## Infrastructure as Code (IaC)
Rapid iteration also involves the ability to recreate your infrastructure quickly and reliably. This is where Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Kubernetes YAML files come into play. They allow you to define your infrastructure in code files, which can be version controlled and quickly deployed, enabling faster and more reliable iterations.
## 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.
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/custom.ipynb)\n",
"\n",
"You can make your own pairwise string evaluators by inheriting from `PairwiseStringEvaluator` class and overwriting the `_evaluate_string_pairs` method (and the `_aevaluate_string_pairs` method if you want to use the evaluator asynchronously).\n",
"\n",
"In this example, you will make a simple custom evaluator that just returns whether the first prediction has more whitespace tokenized 'words' than the second.\n",
"\n",
"You can check out the reference docs for the [PairwiseStringEvaluator interface](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.PairwiseStringEvaluator.html#langchain.evaluation.schema.PairwiseStringEvaluator) for more info.\n"
" prediction=\"The quick brown fox jumped over the lazy dog.\",\n",
" prediction_b=\"The quick brown fox jumped over the dog.\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d90f128f-6f49-42a1-b05a-3aea568ee03b",
"metadata": {},
"source": [
"## LLM-Based Example\n",
"\n",
"That example was simple to illustrate the API, but it wasn't very useful in practice. Below, use an LLM with some custom instructions to form a simple preference scorer similar to the built-in [PairwiseStringEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain). We will use `ChatAnthropic` for the evaluator chain."
" \"\"\"Which option is preferred? Do not take order into account. Evaluate based on accuracy and helpfulness. If neither is preferred, respond with C. Provide your reasoning, then finish with Preference: A/B/C\n",
"\n",
"Input: How do I get the path of the parent directory in python 3.8?\n",
"Reasoning: Both options return the same result. However, since option B is more concise and easily understand, it is preferred.\n",
"Preference: B\n",
"\n",
"Which option is preferred? Do not take order into account. Evaluate based on accuracy and helpfulness. If neither is preferred, respond with C. Provide your reasoning, then finish with Preference: A/B/C\n",
"{'reasoning': 'Option B is preferred over option A for importing from a relative directory, because it is more straightforward and concise.\\n\\nOption A uses the importlib module, which allows importing a module by specifying the full name as a string. While this works, it is less clear compared to option B.\\n\\nOption B directly imports from the relative path using dot notation, which clearly shows that it is a relative import. This is the recommended way to do relative imports in Python.\\n\\nIn summary, option B is more accurate and helpful as it uses the standard Python relative import syntax.',\n",
" 'value': 'B',\n",
" 'score': 0.0}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_string_pairs(\n",
" input=\"How do I import from a relative directory?\",\n",
Comparison evaluators in LangChain help measure two different chains or LLM outputs. These evaluators are helpful for comparative analyses, such as A/B testing between two language models, or comparing different versions of the same model. They can also be useful for things like generating preference scores for ai-assisted reinforcement learning.
These evaluators inherit from the `PairwiseStringEvaluator` class, providing a comparison interface for two strings - typically, the outputs from two different prompts or models, or two versions of the same model. In essence, a comparison evaluator performs an evaluation on a pair of strings and returns a dictionary containing the evaluation score and other relevant details.
To create a custom comparison evaluator, inherit from the `PairwiseStringEvaluator` class and overwrite the `_evaluate_string_pairs` method. If you require asynchronous evaluation, also overwrite the `_aevaluate_string_pairs` method.
Here's a summary of the key methods and properties of a comparison evaluator:
- `evaluate_string_pairs`: Evaluate the output string pairs. This function should be overwritten when creating custom evaluators.
- `aevaluate_string_pairs`: Asynchronously evaluate the output string pairs. This function should be overwritten for asynchronous evaluation.
- `requires_input`: This property indicates whether this evaluator requires an input string.
- `requires_reference`: This property specifies whether this evaluator requires a reference label.
:::note LangSmith Support
The [run_on_dataset](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.smith) evaluation method is designed to evaluate only a single model at a time, and thus, doesn't support these evaluators.
:::
Detailed information about creating custom evaluators and the available built-in comparison evaluators is provided in the following sections.
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
"\n",
"One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
"\n",
"You can load the `pairwise_embedding_distance` evaluator to do this.\n",
"\n",
"**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the outputs are, according to their embedded representation.\n",
"\n",
"Check out the reference docs for the [PairwiseEmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain) for more info."
" prediction=\"Seattle is hot in June\", prediction_b=\"Seattle is cool in June.\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'score': 0.21018880025138598}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hf_evaluator.evaluate_string_pairs(\n",
" prediction=\"Seattle is warm in June\", prediction_b=\"Seattle is cool in June.\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"cite_note-1\"></a><i>1. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the `PairwiseStringDistanceEvalChain`), though it tends to be less reliable than evaluators that use the LLM directly (such as the `PairwiseStringEvalChain`) </i>"
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
"\n",
"Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The `StringComparison` evaluators facilitate this so you can answer questions like:\n",
"\n",
"- Which LLM or prompt produces a preferred output for a given question?\n",
"- Which examples should I include for few-shot example selection?\n",
"- Which output is better to include for fine-tuning?\n",
"\n",
"The simplest and often most reliable automated way to choose a preferred prediction for a given input is to use the `pairwise_string` evaluator.\n",
"\n",
"Check out the reference docs for the [PairwiseStringEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain) for more info."
"{'reasoning': 'Both responses are relevant to the question asked, as they both provide a numerical answer to the question about the number of dogs in the park. However, Response A is incorrect according to the reference answer, which states that there are four dogs. Response B, on the other hand, is correct as it matches the reference answer. Neither response demonstrates depth of thought, as they both simply provide a numerical answer without any additional information or context. \\n\\nBased on these criteria, Response B is the better response.\\n',\n",
" 'value': 'B',\n",
" 'score': 0}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_string_pairs(\n",
" prediction=\"there are three dogs\",\n",
" prediction_b=\"4\",\n",
" input=\"how many dogs are in the park?\",\n",
" reference=\"four\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7491d2e6-4e77-4b17-be6b-7da966785c1d",
"metadata": {},
"source": [
"## Methods\n",
"\n",
"\n",
"The pairwise string evaluator can be called using [evaluate_string_pairs](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.evaluate_string_pairs) (or async [aevaluate_string_pairs](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.aevaluate_string_pairs)) methods, which accept:\n",
"\n",
"- prediction (str) – The predicted response of the first model, chain, or prompt.\n",
"- prediction_b (str) – The predicted response of the second model, chain, or prompt.\n",
"- input (str) – The input question, prompt, or other text.\n",
"- reference (str) – (Only for the labeled_pairwise_string variant) The reference response.\n",
"\n",
"They return a dictionary with the following values:\n",
"\n",
"- value: 'A' or 'B', indicating whether `prediction` or `prediction_b` is preferred, respectively\n",
"- score: Integer 0 or 1 mapped from the 'value', where a score of 1 would mean that the first `prediction` is preferred, and a score of 0 would mean `prediction_b` is preferred.\n",
"- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
]
},
{
"cell_type": "markdown",
"id": "ed353b93-be71-4479-b9c0-8c97814c2e58",
"metadata": {},
"source": [
"## Without References\n",
"\n",
"When references aren't available, you can still predict the preferred response.\n",
"The results will reflect the evaluation model's preference, which is less reliable and may result\n",
"{'reasoning': 'Both responses are correct and relevant to the question. However, Response B is more helpful and insightful as it provides a more detailed explanation of what addition is. Response A is correct but lacks depth as it does not explain what the operation of addition entails. \\n\\nFinal Decision: [[B]]',\n",
" 'value': 'B',\n",
" 'score': 0}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_string_pairs(\n",
" prediction=\"Addition is a mathematical operation.\",\n",
" prediction_b=\"Addition is a mathematical operation that adds two numbers to create a third number, the 'sum'.\",\n",
" input=\"What is addition?\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "4a09b21d-9851-47e8-93d3-90044b2945b0",
"metadata": {
"tags": []
},
"source": [
"## Defining the Criteria\n",
"\n",
"By default, the LLM is instructed to select the 'preferred' response based on helpfulness, relevance, correctness, and depth of thought. You can customize the criteria by passing in a `criteria` argument, where the criteria could take any of the following forms:\n",
"\n",
"- [`Criteria`](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.Criteria.html#langchain.evaluation.criteria.eval_chain.Criteria) enum or its string value - to use one of the default criteria and their descriptions\n",
"- [Constitutional principal](https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html#langchain.chains.constitutional_ai.models.ConstitutionalPrinciple) - use one any of the constitutional principles defined in langchain\n",
"- Dictionary: a list of custom criteria, where the key is the name of the criteria, and the value is the description.\n",
"- A list of criteria or constitutional principles - to combine multiple criteria in one.\n",
"\n",
"Below is an example for determining preferred writing responses based on a custom style."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8539e7d9-f7b0-4d32-9c45-593a7915c093",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"custom_criteria = {\n",
" \"simplicity\": \"Is the language straightforward and unpretentious?\",\n",
" \"clarity\": \"Are the sentences clear and easy to understand?\",\n",
" \"precision\": \"Is the writing precise, with no unnecessary words or details?\",\n",
" \"truthfulness\": \"Does the writing feel honest and sincere?\",\n",
" \"subtext\": \"Does the writing suggest deeper meanings or themes?\",\n",
"{'reasoning': 'Response A is simple, clear, and precise. It uses straightforward language to convey a deep and sincere message about families. The metaphor of joy and sorrow as music is effective and easy to understand.\\n\\nResponse B, on the other hand, is more complex and less clear. The language is more pretentious, with words like \"domicile,\" \"resounds,\" \"abode,\" \"dissonant,\" and \"elegy.\" While it conveys a similar message to Response A, it does so in a more convoluted way. The precision is also lacking due to the use of unnecessary words and details.\\n\\nBoth responses suggest deeper meanings or themes about the shared joy and unique sorrow in families. However, Response A does so in a more effective and accessible way.\\n\\nTherefore, the better response is [[A]].',\n",
" 'value': 'A',\n",
" 'score': 1}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_string_pairs(\n",
" prediction=\"Every cheerful household shares a similar rhythm of joy; but sorrow, in each household, plays a unique, haunting melody.\",\n",
" prediction_b=\"Where one finds a symphony of joy, every domicile of happiness resounds in harmonious,\"\n",
" \" identical notes; yet, every abode of despair conducts a dissonant orchestra, each\"\n",
" \" playing an elegy of grief that is peculiar and profound to its own existence.\",\n",
" input=\"Write some prose about families.\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "a25b60b2-627c-408a-be4b-a2e5cbc10726",
"metadata": {},
"source": [
"## Customize the LLM\n",
"\n",
"By default, the loader uses `gpt-4` in the evaluation chain. You can customize this when loading."
"{'reasoning': 'Here is my assessment:\\n\\nResponse B is more helpful, insightful, and accurate than Response A. Response B simply states \"4\", which directly answers the question by providing the exact number of dogs mentioned in the reference answer. In contrast, Response A states \"there are three dogs\", which is incorrect according to the reference answer. \\n\\nIn terms of helpfulness, Response B gives the precise number while Response A provides an inaccurate guess. For relevance, both refer to dogs in the park from the question. However, Response B is more correct and factual based on the reference answer. Response A shows some attempt at reasoning but is ultimately incorrect. Response B requires less depth of thought to simply state the factual number.\\n\\nIn summary, Response B is superior in terms of helpfulness, relevance, correctness, and depth. My final decision is: [[B]]\\n',\n",
" 'value': 'B',\n",
" 'score': 0}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_string_pairs(\n",
" prediction=\"there are three dogs\",\n",
" prediction_b=\"4\",\n",
" input=\"how many dogs are in the park?\",\n",
" reference=\"four\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e0e89c13-d0ad-4f87-8fcb-814399bafa2a",
"metadata": {},
"source": [
"## Customize the Evaluation Prompt\n",
"\n",
"You can use your own custom evaluation prompt to add more task-specific instructions or to instruct the evaluator to score the output.\n",
"\n",
"*Note: If you use a prompt that expects generates a result in a unique format, you may also have to pass in a custom output parser (`output_parser=your_parser()`) instead of the default `PairwiseStringResultOutputParser`"
"input_variables=['prediction', 'reference', 'prediction_b', 'input'] output_parser=None partial_variables={'criteria': 'helpfulness: Is the submission helpful, insightful, and appropriate?\\nrelevance: Is the submission referring to a real quote from the text?\\ncorrectness: Is the submission correct, accurate, and factual?\\ndepth: Does the submission demonstrate depth of thought?'} template='Given the input context, which do you prefer: A or B?\\nEvaluate based on the following criteria:\\n{criteria}\\nReason step by step and finally, respond with either [[A]] or [[B]] on its own line.\\n\\nDATA\\n----\\ninput: {input}\\nreference: {reference}\\nA: {prediction}\\nB: {prediction_b}\\n---\\nReasoning:\\n\\n' template_format='f-string' validate_template=True\n"
]
}
],
"source": [
"# The prompt was assigned to the evaluator\n",
"print(evaluator.prompt)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9467bb42-7a31-4071-8f66-9ed2c6f06dcd",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'reasoning': 'Helpfulness: Both A and B are helpful as they provide a direct answer to the question.\\nRelevance: A is relevant as it refers to the correct name of the dog from the text. B is not relevant as it provides a different name.\\nCorrectness: A is correct as it accurately states the name of the dog. B is incorrect as it provides a different name.\\nDepth: Both A and B demonstrate a similar level of depth as they both provide a straightforward answer to the question.\\n\\nGiven these evaluations, the preferred response is:\\n',\n",
" 'value': 'A',\n",
" 'score': 1}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluator.evaluate_string_pairs(\n",
" prediction=\"The dog that ate the ice cream was named fido.\",\n",
" prediction_b=\"The dog's name is spot\",\n",
" input=\"What is the name of the dog that ate the ice cream?\",\n",
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/examples/comparisons.ipynb)\n",
"\n",
"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 evaluation, 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 results 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."
"pref_ratios = {k: v / len(preferences) for k, v in counts.items()}\n",
"for k, v in pref_ratios.items():\n",
" print(f\"{name_map.get(k)}: {v:.2%}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimate Confidence Intervals\n",
"\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."
"The \"OpenAI Functions Agent\" would be preferred between 83.18% and 100.00% percent of the time (with 95% confidence).\n",
"The \"Structured Chat Agent\" would be preferred between 0.00% and 16.82% percent of the time (with 95% confidence).\n"
]
}
],
"source": [
"for which_, name in name_map.items():\n",
" low, high = wilson_score_interval(preferences, which=which_)\n",
" print(\n",
" f'The \"{name}\" would be preferred between {low:.2%} and {high:.2%} percent of the time (with 95% confidence).'\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Print out the p-value.**"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The p-value is 0.00000. 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 0.00038% chance of observing the OpenAI Functions Agent be preferred at least 19\n",
"times out of 19 trials.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/ipykernel_15978/384907688.py:6: DeprecationWarning: 'binom_test' is deprecated in favour of 'binomtest' from version 1.7.0 and will be removed in Scipy 1.12.0.\n",
" p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n"
"p_value = stats.binom_test(successes, n, p=0.5, alternative=\"two-sided\")\n",
"print(\n",
" 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.\"\"\"\n",
")"
]
},
{
"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._"
Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks.
The guides in this section review the APIs and functionality LangChain provides to help you better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
These built-in evaluators all integrate smoothly with [LangSmith](/docs/langsmith), and allow you to create feedback loops that improve your application over time and prevent regressions.
Each evaluator type in LangChain comes with ready-to-use implementations and an extensible API that allows for customization according to your unique requirements. Here are some of the types of evaluators we offer:
- [String Evaluators](/docs/guides/productionization/evaluation/string/): These evaluators assess the predicted string for a given input, usually comparing it against a reference string.
- [Trajectory Evaluators](/docs/guides/productionization/evaluation/trajectory/): These are used to evaluate the entire trajectory of agent actions.
- [Comparison Evaluators](/docs/guides/productionization/evaluation/comparison/): These evaluators are designed to compare predictions from two runs on a common input.
These evaluators can be used across various scenarios and can be applied to different chain and LLM implementations in the LangChain library.
We also are working to share guides and cookbooks that demonstrate how to use these evaluators in real-world scenarios, such as:
- [Chain Comparisons](/docs/guides/productionization/evaluation/examples/comparisons): This example uses a comparison evaluator to predict the preferred output. It reviews ways to measure confidence intervals to select statistically significant differences in aggregate preference scores across different models or prompts.
## LangSmith Evaluation
LangSmith provides an integrated evaluation and tracing framework that allows you to check for regressions, compare systems, and easily identify and fix any sources of errors and performance issues. Check out the docs on [LangSmith Evaluation](https://docs.smith.langchain.com/evaluation) and additional [cookbooks](https://docs.smith.langchain.com/cookbook) for more detailed information on evaluating your applications.
## LangChain benchmarks
Your application quality is a function both of the LLM you choose and the prompting and data retrieval strategies you employ to provide model contexet. We have published a number of benchmark tasks within the [LangChain Benchmarks](https://langchain-ai.github.io/langchain-benchmarks/) package to grade different LLM systems on tasks such as:
- Agent tool use
- Retrieval-augmented question-answering
- Structured Extraction
Check out the docs for examples and leaderboard information.
## Reference Docs
For detailed information on the available evaluators, including how to instantiate, configure, and customize them, check out the [reference documentation](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.evaluation) directly.
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb)\n",
"\n",
"In scenarios where you wish to assess a model's output using a specific rubric or criteria set, the `criteria` evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain's output complies with a defined set of criteria.\n",
"\n",
"To understand its functionality and configurability in depth, refer to the reference documentation of the [CriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain) class.\n",
"\n",
"### Usage without references\n",
"\n",
"In this example, you will use the `CriteriaEvalChain` to check whether an output is concise. First, create the evaluation chain to predict whether outputs are \"concise\"."
"{'reasoning': 'The criterion is conciseness, which means the submission should be brief and to the point. \\n\\nLooking at the submission, the answer to the question \"What\\'s 2+2?\" is indeed \"four\". However, the respondent has added extra information, stating \"That\\'s an elementary question.\" This statement does not contribute to answering the question and therefore makes the response less concise.\\n\\nTherefore, the submission does not meet the criterion of conciseness.\\n\\nN', 'value': 'N', 'score': 0}\n"
]
}
],
"source": [
"eval_result = evaluator.evaluate_strings(\n",
" prediction=\"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.\",\n",
" input=\"What's 2+2?\",\n",
")\n",
"print(eval_result)"
]
},
{
"cell_type": "markdown",
"id": "35e61e4d-b776-4f6b-8c89-da5d3604134a",
"metadata": {},
"source": [
"#### Output Format\n",
"\n",
"All string evaluators expose an [evaluate_strings](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html?highlight=evaluate_strings#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.evaluate_strings) (or async [aevaluate_strings](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html?highlight=evaluate_strings#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.aevaluate_strings)) method, which accepts:\n",
"\n",
"- input (str) – The input to the agent.\n",
"- prediction (str) – The predicted response.\n",
"\n",
"The criteria evaluators return a dictionary with the following values:\n",
"- score: Binary integer 0 to 1, where 1 would mean that the output is compliant with the criteria, and 0 otherwise\n",
"- value: A \"Y\" or \"N\" corresponding to the score\n",
"- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
]
},
{
"cell_type": "markdown",
"id": "c40b1ac7-8f95-48ed-89a2-623bcc746461",
"metadata": {},
"source": [
"## Using Reference Labels\n",
"\n",
"Some criteria (such as correctness) require reference labels to work correctly. To do this, initialize the `labeled_criteria` evaluator and call the evaluator with a `reference` string."
"Most of the time, you'll want to define your own custom criteria (see below), but we also provide some common criteria you can load with a single string.\n",
"Here's a list of pre-implemented criteria. Note that in the absence of labels, the LLM merely predicts what it thinks the best answer is and is not grounded in actual law or context."
"# For a list of other default supported criteria, try calling `supported_default_criteria`\n",
"list(Criteria)"
]
},
{
"cell_type": "markdown",
"id": "077c4715-e857-44a3-9f87-346642586a8d",
"metadata": {},
"source": [
"## Custom Criteria\n",
"\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: it's recommended that you create a single evaluator per criterion. This way, separate feedback can be provided for each aspect. Additionally, if you provide antagonistic criteria, the evaluator won't be very useful, as it will be configured to predict compliance for ALL of the criteria provided."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "bafa0a11-2617-4663-84bf-24df7d0736be",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': \"The criterion asks if the output contains numeric or mathematical information. The joke in the submission does contain mathematical information. It refers to the mathematical concept of squaring a number and also mentions 'pi', which is a mathematical constant. Therefore, the submission does meet the criterion.\\n\\nY\", 'value': 'Y', 'score': 1}\n",
"{'reasoning': 'Let\\'s assess the submission based on the given criteria:\\n\\n1. Numeric: The output does not contain any explicit numeric information. The word \"square\" and \"pi\" are mathematical terms but they are not numeric information per se.\\n\\n2. Mathematical: The output does contain mathematical information. The terms \"square\" and \"pi\" are mathematical terms. The joke is a play on the mathematical concept of squaring a number (in this case, pi).\\n\\n3. Grammatical: The output is grammatically correct. The sentence structure, punctuation, and word usage are all correct.\\n\\n4. Logical: The output is logical. It makes sense within the context of the joke. The joke is a play on words between the mathematical concept of squaring a number (pi) and eating a square pie.\\n\\nBased on the above analysis, the submission does not meet all the criteria because it does not contain numeric information.\\nN', 'value': 'N', 'score': 0}\n"
]
}
],
"source": [
"custom_criterion = {\n",
" \"numeric\": \"Does the output contain numeric or mathematical information?\"\n",
"}\n",
"\n",
"eval_chain = load_evaluator(\n",
" EvaluatorType.CRITERIA,\n",
" criteria=custom_criterion,\n",
")\n",
"query = \"Tell me a joke\"\n",
"prediction = \"I ate some square pie but I don't know the square of pi.\"\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": 12,
"id": "f4061bc0-a88a-4d1c-88a0-8003bba084c7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'reasoning': 'The criterion asks to identify if the assistant\\'s response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.\\n\\nLooking at the assistant\\'s response, it is clear that it is not racist or sexist as it does not discriminate or stereotype based on race or gender. \\n\\nThe response is also not illegal as it does not involve any criminal activity or encourage any form of illegal behavior.\\n\\nThe response is not dangerous as it does not pose a physical threat or risk to anyone\\'s safety.\\n\\nHowever, the assistant\\'s response can be considered harmful and toxic as it uses derogatory language (\"lilly-livered nincompoop\") to describe \\'Will\\'. This can be seen as a form of verbal abuse or insult, which can cause emotional harm.\\n\\nThe response can also be seen as unethical, as it is generally considered inappropriate to insult or belittle someone in this manner.\\n\\nN', 'value': 'N', 'score': 0}\n"
" prediction=\"I say that man is a lilly-livered nincompoop\",\n",
" input=\"What do you think of Will?\",\n",
")\n",
"print(eval_result)"
]
},
{
"cell_type": "markdown",
"id": "ae60b5e3-ceac-46b1-aabb-ee36930cb57c",
"metadata": {
"tags": []
},
"source": [
"## Configuring the LLM\n",
"\n",
"If you don't specify an eval LLM, the `load_evaluator` method will initialize a `gpt-4` LLM to power the grading chain. Below, use an anthropic model instead."
"{'reasoning': 'Step 1) Analyze the conciseness criterion: Is the submission concise and to the point?\\nStep 2) The submission provides extraneous information beyond just answering the question directly. It characterizes the question as \"elementary\" and provides reasoning for why the answer is 4. This additional commentary makes the submission not fully concise.\\nStep 3) Therefore, based on the analysis of the conciseness criterion, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n"
]
}
],
"source": [
"eval_result = evaluator.evaluate_strings(\n",
" prediction=\"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.\",\n",
" input=\"What's 2+2?\",\n",
")\n",
"print(eval_result)"
]
},
{
"cell_type": "markdown",
"id": "5e7fc7bb-3075-4b44-9c16-3146a39ae497",
"metadata": {},
"source": [
"# Configuring the Prompt\n",
"\n",
"If you want to completely customize the prompt, you can initialize the evaluator with a custom prompt template as follows."
"fstring = \"\"\"Respond Y or N based on how well the following response follows the specified rubric. Grade only based on the rubric and expected response:\n",
"\n",
"Grading Rubric: {criteria}\n",
"Expected Response: {reference}\n",
"\n",
"DATA:\n",
"---------\n",
"Question: {input}\n",
"Response: {output}\n",
"---------\n",
"Write out your explanation for each criterion, then respond with Y or N on a new line.\"\"\"\n",
"{'reasoning': 'Correctness: No, the response is not correct. The expected response was \"It\\'s 17 now.\" but the response given was \"What\\'s 2+2? That\\'s an elementary question. The answer you\\'re looking for is that two and two is four.\"', 'value': 'N', 'score': 0}\n"
]
}
],
"source": [
"eval_result = evaluator.evaluate_strings(\n",
" prediction=\"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.\",\n",
" input=\"What's 2+2?\",\n",
" reference=\"It's 17 now.\",\n",
")\n",
"print(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."
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/custom.ipynb)\n",
"\n",
"You can make your own custom string evaluators by inheriting from the `StringEvaluator` class and implementing the `_evaluate_strings` (and `_aevaluate_strings` for async support) methods.\n",
"\n",
"In this example, you will create a perplexity evaluator using the HuggingFace [evaluate](https://huggingface.co/docs/evaluate/index) library.\n",
"[Perplexity](https://en.wikipedia.org/wiki/Perplexity) is a measure of how well the generated text would be predicted by the model used to compute the metric."
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/embedding_distance.ipynb)\n",
"\n",
"To measure semantic similarity (or dissimilarity) between a prediction and a reference label string, you could use a vector distance metric the two embedded representations using the `embedding_distance` evaluator.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
"\n",
"\n",
"**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the prediction is to the reference, according to their embedded representation.\n",
"\n",
"Check out the reference docs for the [EmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.EmbeddingDistanceEvalChain) for more info."
"hf_evaluator.evaluate_strings(prediction=\"I shall go\", reference=\"I will go\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"cite_note-1\"></a><i>1. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain)), though it tends to be less reliable than evaluators that use the LLM directly (such as the [QAEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.qa.eval_chain.QAEvalChain.html#langchain.evaluation.qa.eval_chain.QAEvalChain) or [LabeledCriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.LabeledCriteriaEvalChain)) </i>"
"[](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/exact_match.ipynb)\n",
"\n",
"Probably the simplest ways to evaluate an LLM or runnable's string output against a reference label is by a simple string equivalence.\n",
"\n",
"This can be accessed using the `exact_match` evaluator."
Some files were not shown because too many files have changed in this diff
Show More
Reference in New Issue
Block a user
Blocking a user prevents them from interacting with repositories, such as opening or commenting on pull requests or issues. Learn more about blocking a user.
