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Merge branch 'master' into rig-int
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David Myriel
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---
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title: Frameworks
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weight: 20
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partition: build
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---
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## Framework Integrations
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| Framework | Description |
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| ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
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| [AutoGen](/documentation/frameworks/autogen/) | Framework from Microsoft building LLM applications using multiple conversational agents. |
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| [Canopy](/documentation/frameworks/canopy/) | Framework from Pinecone for building RAG applications using LLMs and knowledge bases. |
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| [Cheshire Cat](/documentation/frameworks/cheshire-cat/) | Framework to create personalized AI assistants using custom data. |
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| [DocArray](/documentation/frameworks/docarray/) | Python library for managing data in multi-modal AI applications. |
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| [DSPy](/documentation/frameworks/dspy/) | Framework for algorithmically optimizing LM prompts and weights. |
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| [Fifty-One](/documentation/frameworks/fifty-one/) | Toolkit for building high-quality datasets and computer vision models. |
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| [Genkit](/documentation/frameworks/genkit/) | Framework to build, deploy, and monitor production-ready AI-powered apps. |
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| [Haystack](/documentation/frameworks/haystack/) | LLM orchestration framework to build customizable, production-ready LLM applications. |
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| [Lakechain](/documentation/frameworks/lakechain/) | Python framework for deploying document processing pipelines on AWS using infrastructure-as-code. |
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| [Langchain](/documentation/frameworks/langchain/) | Python framework for building context-aware, reasoning applications using LLMs. |
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| [Langchain-Go](/documentation/frameworks/langchain-go/) | Go framework for building context-aware, reasoning applications using LLMs. |
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| [Langchain4j](/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
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| [LlamaIndex](/documentation/frameworks/llama-index/) | A data framework for building LLM applications with modular integrations. |
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| [Mem0](/documentation/frameworks/mem0/) | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
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| [MemGPT](/documentation/frameworks/memgpt/) | System to build LLM agents with long term memory & custom tools |
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| [Pandas-AI](/documentation/frameworks/pandas-ai/) | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
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| [Rig-rs](/documentation/frameworks/rig-rs/) | Rust library for building scalable, modular, and ergonomic LLM-powered applications. |
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| [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. |
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| [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
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| [Sycamore](/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
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| [txtai](/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
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| [Vanna AI](/documentation/frameworks/vanna-ai/) | Python RAG framework for SQL generation and querying. |
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| Framework | Description |
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| ------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------- |
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| [AutoGen](/documentation/frameworks/autogen/) | Framework from Microsoft building LLM applications using multiple conversational agents. |
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| [Canopy](/documentation/frameworks/canopy/) | Framework from Pinecone for building RAG applications using LLMs and knowledge bases. |
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| [Cheshire Cat](/documentation/frameworks/cheshire-cat/) | Framework to create personalized AI assistants using custom data. |
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| [DocArray](/documentation/frameworks/docarray/) | Python library for managing data in multi-modal AI applications. |
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| [DSPy](/documentation/frameworks/dspy/) | Framework for algorithmically optimizing LM prompts and weights. |
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| [Feast](/documentation/frameworks/feast/) | Open-source feature store to operate production ML systems at scale as a set of features. |
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| [Fifty-One](/documentation/frameworks/fifty-one/) | Toolkit for building high-quality datasets and computer vision models. |
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| [Genkit](/documentation/frameworks/genkit/) | Framework to build, deploy, and monitor production-ready AI-powered apps. |
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| [Haystack](/documentation/frameworks/haystack/) | LLM orchestration framework to build customizable, production-ready LLM applications. |
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| [Lakechain](/documentation/frameworks/lakechain/) | Python framework for deploying document processing pipelines on AWS using infrastructure-as-code. |
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| [Langchain](/documentation/frameworks/langchain/) | Python framework for building context-aware, reasoning applications using LLMs. |
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| [Langchain-Go](/documentation/frameworks/langchain-go/) | Go framework for building context-aware, reasoning applications using LLMs. |
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| [Langchain4j](/documentation/frameworks/langchain4j/) | Java framework for building context-aware, reasoning applications using LLMs. |
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| [LlamaIndex](/documentation/frameworks/llama-index/) | A data framework for building LLM applications with modular integrations. |
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| [Mem0](/documentation/frameworks/mem0/) | Self-improving memory layer for LLM applications, enabling personalized AI experiences. |
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| [MemGPT](/documentation/frameworks/memgpt/) | System to build LLM agents with long term memory & custom tools |
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| [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/) | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. |
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| [Pandas-AI](/documentation/frameworks/pandas-ai/) | Python library to query/visualize your data (CSV, XLSX, PostgreSQL, etc.) in natural language |
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| [Ragbits](/documentation/frameworks/ragbits/) | Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications. |
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| [Rig-rs](/documentation/frameworks/rig-rs/) | Rust library for building scalable, modular, and ergonomic LLM-powered applications. |
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| [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. |
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| [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. |
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| [Sycamore](/documentation/frameworks/sycamore/) | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data. |
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| [txtai](/documentation/frameworks/txtai/) | Python library for semantic search, LLM orchestration and language model workflows. |
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| [Vanna AI](/documentation/frameworks/vanna-ai/) | Python RAG framework for SQL generation and querying. |
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---
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title: Feast
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---
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## Feast
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[Feast (**Fe**ature **St**ore)](https://docs.feast.dev) is an open-source feature store that helps teams operate production ML systems at scale by allowing them to define, manage, validate, and serve features for production AI/ML.
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Qdrant is available as a supported vectorstore in Feast to integrate in your workflows.
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## Insatallation
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To use the Qdrant online store, you need to install Feast with the `qdrant` extra.
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```bash
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pip install 'feast[qdrant]'
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```
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## Usage
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An example config with Qdrant could look like:
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```yaml
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project: my_feature_repo
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registry: data/registry.db
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provider: local
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online_store:
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type: qdrant
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host: xyz-example.eu-central.aws.cloud.qdrant.io
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port: 6333
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api_key: <your-own-key>
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vector_len: 384
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# Reference: https://qdrant.tech/documentation/concepts/vectors/#named-vectors
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# vector_name: text-vec
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write_batch_size: 100
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```
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You can refer to the Feast [reference](https://rtd.feast.dev/en/master/index.html#) for the full list of configuration options.
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## Retrieving Documents
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The Qdrant online store supports retrieving document vectors for a given list of entity keys. The document vectors are returned as a dictionary where the key is the entity key and the value being the vector.
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```python
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from feast import FeatureStore
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feature_store = FeatureStore(repo_path="feature_store.yaml")
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query_vector = [1.0, 2.0, 3.0, 4.0, 5.0]
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top_k = 5
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feature_values = feature_store.retrieve_online_documents(
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feature="my_feature",
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query=query_vector,
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top_k=top_k
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)
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```
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## 📚 Further Reading
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- [Feast Docs](http://docs.feast.dev/)
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- [Feast Reference](https://rtd.feast.dev/en/master/index.html/)
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- [Source](https://github.com/feast-dev/feast/tree/master/sdk/python/feast/infra/online_stores/)
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---
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title: Neo4j GraphRAG
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---
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# Neo4j GraphRAG
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[Neo4j GraphRAG](https://neo4j.com/docs/neo4j-graphrag-python/current/) is a Python package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. As a first-party library, it offers a robust, feature-rich, and high-performance solution, with the added assurance of long-term support and maintenance directly from Neo4j. It offers a Qdrant retriever natively to search for vectors stored in a Qdrant collection.
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## Installation
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```bash
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pip install neo4j-graphrag[qdrant]
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```
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## Usage
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A vector query with Neo4j and Qdrant could look like:
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```python
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from neo4j import GraphDatabase
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from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
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from qdrant_client import QdrantClient
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from examples.embedding_biology import EMBEDDING_BIOLOGY
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NEO4J_URL = "neo4j://localhost:7687"
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NEO4J_AUTH = ("neo4j", "password")
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with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
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retriever = QdrantNeo4jRetriever(
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driver=neo4j_driver,
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client=QdrantClient(url="http://localhost:6333"),
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collection_name="{collection_name}",
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id_property_external="neo4j_id",
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id_property_neo4j="id",
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)
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retriever.search(query_vector=[0.5523, 0.523, 0.132, 0.523, ...], top_k=5)
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```
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Alternatively, you can use any [Langchain embeddings providers](https://python.langchain.com/docs/integrations/text_embedding/), to vectorize text queries automatically.
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```python
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from langchain_huggingface.embeddings import HuggingFaceEmbeddings
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from neo4j import GraphDatabase
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from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
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from qdrant_client import QdrantClient
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NEO4J_URL = "neo4j://localhost:7687"
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NEO4J_AUTH = ("neo4j", "password")
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with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
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embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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retriever = QdrantNeo4jRetriever(
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driver=neo4j_driver,
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client=QdrantClient(url="http://localhost:6333"),
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collection_name="{collection_name}",
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id_property_external="neo4j_id",
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id_property_neo4j="id",
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embedder=embedder,
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)
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retriever.search(query_text="my user query", top_k=10)
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```
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## Further Reading
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- [Neo4j GraphRAG Reference](https://neo4j.com/docs/neo4j-graphrag-python/current/index.html)
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- [Qdrant Retriever Reference](https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_rag.html#qdrant-neo4j-retriever-user-guide)
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- [Source](https://github.com/neo4j/neo4j-graphrag-python/tree/main/src/neo4j_graphrag/retrievers/external/qdrant)
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---
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title: Ragbits
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---
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# Ragbits
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[Ragbit](https://ragbits.deepsense.ai) is a Python package that offers essential "bits" for building powerful Retrieval-Augmented Generation (RAG) applications. It prioritizes developer experience by providing a simple and intuitive API. It also includes a comprehensive set of tools for seamlessly building, testing, and deploying your RAG applications efficiently.
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Qdrant is available as a vectorstore in Ragbits to ingest and search search documents from a collection.
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## Installation
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Install the Python package that comes bundled with the Qdrant integration.
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```bash
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pip install ragbits
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```
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## Usage
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An example usage of Ragbits and Qdrant would look something like this:
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The following example uses [OpenAI embeddings](https://platform.openai.com/docs/guides/embeddings) via [LiteLLM](https://www.litellm.ai).
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```python
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import asyncio
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from qdrant_client import AsyncQdrantClient
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from ragbits.core.embeddings.litellm import LiteLLMEmbeddings
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from ragbits.core.vector_stores.qdrant import QdrantVectorStore
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from ragbits.document_search import DocumentSearch, SearchConfig
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from ragbits.document_search.documents.document import DocumentMeta
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documents = [
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DocumentMeta.create_text_document_from_literal(
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"RIP boiled water. You will be mist."
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),
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DocumentMeta.create_text_document_from_literal(
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"Why programmers don't like to swim? Because they're scared of the floating points."
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),
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DocumentMeta.create_text_document_from_literal("This one is completely unrelated."),
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]
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async def main() -> None:
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embedder = LiteLLMEmbeddings(
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model="text-embedding-3-small",
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)
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vector_store = QdrantVectorStore(
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client=AsyncQdrantClient(url="http://localhost:6333"),
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collection_name="{collection_name}",
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)
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document_search = DocumentSearch(
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embedder=embedder,
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vector_store=vector_store,
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)
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await document_search.ingest(documents)
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all_documents = await vector_store.list()
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print([doc.metadata["content"] for doc in all_documents])
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query = "I write computer software. Tell me something."
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vector_store_kwargs = {
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"k": 1,
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"max_distance": None,
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}
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results = await document_search.search(
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query,
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config=SearchConfig(vector_store_kwargs=vector_store_kwargs),
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)
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print(f"Documents similar to: {query}")
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print([element.get_key() for element in results])
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```
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</details>
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## 📚 Further Reading
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- Ragbits [Documentation](http://ragbits.deepsense.ai)
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- [Source Code](https://github.com/deepsense-ai/ragbits)
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